master.bib

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@STRING{academic= {Academic Press} }
@STRING{academic= {Academic Press} }
@STRING{academic= {Academic Press} }
@STRING{acmmathsoft={{ACM} Trans. Math. Softw.} }
@STRING{acmmathsoft={{ACM} Trans. Math. Softw.} }
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@STRING{acmpress= {{ACM} Press} }
@STRING{acmpress= {{ACM} Press} }
@STRING{actanumerica={Acta Numer.} }
@STRING{actanumerica={Acta Numer.} }
@STRING{actanumerica={Acta Numer.} }
@STRING{adamhilger={Adam Hilger} }
@STRING{adamhilger={Adam Hilger} }
@STRING{adamhilger={Adam Hilger} }
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@STRING{addisonwesley={Addison-Wesley} }
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@STRING{allynbacon={Allyn and Bacon} }
@STRING{allynbacon={Allyn and Bacon} }
@STRING{allynbacon={Allyn and Bacon} }
@STRING{amermathmonthly={Amer. Math. Monthly} }
@STRING{amermathmonthly={Amer. Math. Monthly} }
@STRING{amermathmonthly={Amer. Math. Monthly} }
@STRING{amersocio={Amer. J. Sociology} }
@STRING{amersocio={Amer. J. Sociology} }
@STRING{amersocio={Amer. J. Sociology} }
@STRING{amerstatassoc={J. Amer. Statist. Assoc.} }
@STRING{amerstatassoc={J. Amer. Statist. Assoc.} }
@STRING{amerstatassoc={J. Amer. Statist. Assoc.} }
@STRING{ams	= {American Mathematical Society} }
@STRING{ams	= {American Mathematical Society} }
@STRING{ams	= {American Mathematical Society} }
@STRING{amstrans= {Amer. Math. Soc. Transl.} }
@STRING{amstrans= {Amer. Math. Soc. Transl.} }
@STRING{amstrans= {Amer. Math. Soc. Transl.} }
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@STRING{annalsstats={Ann. Statist.} }
@STRING{annalsstats={Ann. Statist.} }
@STRING{applmathcomp={Appl. Math. Comput.} }
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@STRING{appnummath={Appl. Numer. Math.} }
@STRING{appnummath={Appl. Numer. Math.} }
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@STRING{athena	= {Athena Scientific} }
@STRING{athena	= {Athena Scientific} }
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@STRING{birkhauser={Birkha{\"u}ser} }
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@STRING{bit	= {{BIT}} }
@STRING{bit	= {{BIT}} }
@STRING{bit	= {{BIT}} }
@STRING{britstatpsych={Brit. J. Math. Statist. Psych.} }
@STRING{britstatpsych={Brit. J. Math. Statist. Psych.} }
@STRING{britstatpsych={Brit. J. Math. Statist. Psych.} }
@STRING{bullams	= {Bull. Amer. Math. Soc.} }
@STRING{bullams	= {Bull. Amer. Math. Soc.} }
@STRING{bullams	= {Bull. Amer. Math. Soc.} }
@STRING{cacm	= {Comm. {ACM}} }
@STRING{cacm	= {Comm. {ACM}} }
@STRING{cacm	= {Comm. {ACM}} }
@STRING{cambridgepress={Cambridge University Press} }
@STRING{cambridgepress={Cambridge University Press} }
@STRING{cambridgepress={Cambridge University Press} }
@STRING{canmathbull={Canad. Math. Bull.} }
@STRING{canmathbull={Canad. Math. Bull.} }
@STRING{canmathbull={Canad. Math. Bull.} }
@STRING{chelsea	= {Chelsea} }
@STRING{chelsea	= {Chelsea} }
@STRING{chelsea	= {Chelsea} }
@STRING{claredonpress={Claredon Press} }
@STRING{claredonpress={Claredon Press} }
@STRING{claredonpress={Claredon Press} }
@STRING{compapplmath={J. Comput. Appl. Math.} }
@STRING{compapplmath={J. Comput. Appl. Math.} }
@STRING{compapplmath={J. Comput. Appl. Math.} }
@STRING{compapplopt={Comput. Optim. Appl.} }
@STRING{compapplopt={Comput. Optim. Appl.} }
@STRING{compapplopt={Comput. Optim. Appl.} }
@STRING{compjour= {Comput. J.} }
@STRING{compjour= {Comput. J.} }
@STRING{compjour= {Comput. J.} }
@STRING{compoptsoft={Comput. Optim. Softw.} }
@STRING{compoptsoft={Comput. Optim. Softw.} }
@STRING{compoptsoft={Comput. Optim. Softw.} }
@STRING{compphys= {J. Comput. Phys.} }
@STRING{compphys= {J. Comput. Phys.} }
@STRING{compphys= {J. Comput. Phys.} }
@STRING{compserv= {Comput. Surveys} }
@STRING{compserv= {Comput. Surveys} }
@STRING{compserv= {Comput. Surveys} }
@STRING{compstruct={Comput. \& Structures} }
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@STRING{compsyssci={J. Comput. System Sci.} }
@STRING{computer= {{IEEE} Computer} }
@STRING{computer= {{IEEE} Computer} }
@STRING{computer= {{IEEE} Computer} }
@STRING{computing={Computing} }
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@STRING{contempmath={Contemp. Math.} }
@STRING{contempmath={Contemp. Math.} }
@STRING{contempmath={Contemp. Math.} }
@STRING{crelle	= {Crelle's J.} }
@STRING{crelle	= {Crelle's J.} }
@STRING{crelle	= {Crelle's J.} }
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@STRING{doverpub= {Dover Publications} }
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@STRING{eyolles	= {Eyolles} }
@STRING{focm	= {Found. Comput. Math.} }
@STRING{focm	= {Found. Comput. Math.} }
@STRING{geophysics={Geophys.} }
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@STRING{geophysics={Geophys.} }
@STRING{giornalemath={Giorn. Mat.} }
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@STRING{holtrinehartwinston={Holt, Rinehart and Winston} }
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@STRING{ieeespec= {{IEEE} Spectrum} }
@STRING{ieeespec= {{IEEE} Spectrum} }
@STRING{ieeespec= {{IEEE} Spectrum} }
@STRING{ieeetransac={{IEEE} Trans. Automat. Control} }
@STRING{ieeetransac={{IEEE} Trans. Automat. Control} }
@STRING{ieeetransac={{IEEE} Trans. Automat. Control} }
@STRING{ieeetransaeroelec={{IEEE} Trans. Aerospace Electron. Systems} }
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@STRING{ieeetransaeroelec={{IEEE} Trans. Aerospace Electron. Systems} }
@STRING{ieeetranscomp={{IEEE} Trans. Comput.} }
@STRING{ieeetranscomp={{IEEE} Trans. Comput.} }
@STRING{ieeetranscomp={{IEEE} Trans. Comput.} }
@STRING{ieeetransimproc={{IEEE} Trans. Image Process.} }
@STRING{ieeetransimproc={{IEEE} Trans. Image Process.} }
@STRING{ieeetransimproc={{IEEE} Trans. Image Process.} }
@STRING{ieeetransinfo={{IEEE} Trans. Inform. Theory} }
@STRING{ieeetransinfo={{IEEE} Trans. Inform. Theory} }
@STRING{ieeetransinfo={{IEEE} Trans. Inform. Theory} }
@STRING{ieeetranssigproc={{IEEE} Trans. Sig. Proc.} }
@STRING{ieeetranssigproc={{IEEE} Trans. Sig. Proc.} }
@STRING{ieeetranssigproc={{IEEE} Trans. Sig. Proc.} }
@STRING{imanumerana={{IMA} J. Numer. Anal.} }
@STRING{imanumerana={{IMA} J. Numer. Anal.} }
@STRING{imanumerana={{IMA} J. Numer. Anal.} }
@STRING{imanumerana-={{IMA} Journal of Numerical Analysis} }
@STRING{imanumerana-={{IMA} Journal of Numerical Analysis} }
@STRING{imanumerana-={{IMA} Journal of Numerical Analysis} }
@STRING{infproclet={Inform. Process. Lett.} }
@STRING{infproclet={Inform. Process. Lett.} }
@STRING{infproclet={Inform. Process. Lett.} }
@STRING{instmathapp={J. Inst. Math. Appl.} }
@STRING{instmathapp={J. Inst. Math. Appl.} }
@STRING{instmathapp={J. Inst. Math. Appl.} }
@STRING{intcontrol={Internat. J. Control} }
@STRING{intcontrol={Internat. J. Control} }
@STRING{intcontrol={Internat. J. Control} }
@STRING{interscience={Interscience} }
@STRING{interscience={Interscience} }
@STRING{interscience={Interscience} }
@STRING{intnumereng={Internat. J. Numer. Methods Engrg.} }
@STRING{intnumereng={Internat. J. Numer. Methods Engrg.} }
@STRING{intnumereng={Internat. J. Numer. Methods Engrg.} }
@STRING{intsuper= {Internat. J. Supercomputing Applic.} }
@STRING{intsuper= {Internat. J. Supercomputing Applic.} }
@STRING{intsuper= {Internat. J. Supercomputing Applic.} }
@STRING{jacm	= {J. Assoc. Comput. Mach.} }
@STRING{jacm	= {J. Assoc. Comput. Mach.} }
@STRING{jacm	= {J. Assoc. Comput. Mach.} }
@STRING{jconvexanal={J. Convex Anal.} }
@STRING{jconvexanal={J. Convex Anal.} }
@STRING{jconvexanal={J. Convex Anal.} }
@STRING{johnshopkinspress={The Johns Hopkins University Press} }
@STRING{johnshopkinspress={The Johns Hopkins University Press} }
@STRING{johnshopkinspress={The Johns Hopkins University Press} }
@STRING{johnwileysons={John Wiley and Sons} }
@STRING{johnwileysons={John Wiley and Sons} }
@STRING{johnwileysons={John Wiley and Sons} }
@STRING{jota	= {J. Optim. Theory Appl.} }
@STRING{jota	= {J. Optim. Theory Appl.} }
@STRING{jota	= {J. Optim. Theory Appl.} }
@STRING{jresnatburstand={J. Res. Nat. Bur. Standards} }
@STRING{jresnatburstand={J. Res. Nat. Bur. Standards} }
@STRING{jresnatburstand={J. Res. Nat. Bur. Standards} }
@STRING{jsiam	= {J. Soc. Indust. Appl. Math.} }
@STRING{jsiam	= {J. Soc. Indust. Appl. Math.} }
@STRING{jsiam	= {J. Soc. Indust. Appl. Math.} }
@STRING{jsiamb	= {J. Soc. Indust. Appl. Math. Ser. B Numer. Anal.} }
@STRING{jsiamb	= {J. Soc. Indust. Appl. Math. Ser. B Numer. Anal.} }
@STRING{jsiamb	= {J. Soc. Indust. Appl. Math. Ser. B Numer. Anal.} }
@STRING{kibernetika={Kibernetika} }
@STRING{kibernetika={Kibernetika} }
@STRING{kibernetika={Kibernetika} }
@STRING{linalgapp={Linear Algebra Appl.} }
@STRING{linalgapp={Linear Algebra Appl.} }
@STRING{linalgapp={Linear Algebra Appl.} }
@STRING{macmillan={Macmillan} }
@STRING{macmillan={Macmillan} }
@STRING{macmillan={Macmillan} }
@STRING{managesci={Mgmnt Sci.} }
@STRING{managesci={Mgmnt Sci.} }
@STRING{managesci={Mgmnt Sci.} }
@STRING{mathanaappl={J. Math. Anal. Appl.} }
@STRING{mathanaappl={J. Math. Anal. Appl.} }
@STRING{mathanaappl={J. Math. Anal. Appl.} }
@STRING{mathannalen={Math. Ann.} }
@STRING{mathannalen={Math. Ann.} }
@STRING{mathannalen={Math. Ann.} }
@STRING{mathcomp= {Math. Comp.} }
@STRING{mathcomp= {Math. Comp.} }
@STRING{mathcomp= {Math. Comp.} }
@STRING{mathofor= {Math. Op. Res.} }
@STRING{mathofor= {Math. Op. Res.} }
@STRING{mathofor= {Math. Op. Res.} }
@STRING{mathphys= {J. Math. Phys.} }
@STRING{mathphys= {J. Math. Phys.} }
@STRING{mathphys= {J. Math. Phys.} }
@STRING{mathprog= {Math. Program.} }
@STRING{mathprog= {Math. Program.} }
@STRING{mathprog= {Math. Program.} }
@STRING{mathprogb={Math. Program., Ser. B} }
@STRING{mathprogb={Math. Program., Ser. B} }
@STRING{mathprogb={Math. Program., Ser. B} }
@STRING{mathprogc={Math. Program. Comp.} }
@STRING{mathprogc={Math. Program. Comp.} }
@STRING{mathprogc={Math. Program. Comp.} }
@STRING{mathprogstudy={Math. Program. Study} }
@STRING{mathprogstudy={Math. Program. Study} }
@STRING{mathprogstudy={Math. Program. Study} }
@STRING{mathscand={Math. Scand.} }
@STRING{mathscand={Math. Scand.} }
@STRING{mathscand={Math. Scand.} }
@STRING{mathworks={The Math Works Inc.} }
@STRING{mathworks={The Math Works Inc.} }
@STRING{mathworks={The Math Works Inc.} }
@STRING{mcgrawhill={McGraw-Hill} }
@STRING{mcgrawhill={McGraw-Hill} }
@STRING{mcgrawhill={McGraw-Hill} }
@STRING{natburstd={National Bureau of Standards} }
@STRING{natburstd={National Bureau of Standards} }
@STRING{natburstd={National Bureau of Standards} }
@STRING{northholland={North-Holland} }
@STRING{northholland={North-Holland} }
@STRING{northholland={North-Holland} }
@STRING{numermath={Numer. Math.} }
@STRING{numermath={Numer. Math.} }
@STRING{numermath={Numer. Math.} }
@STRING{optimmeth={Optim. Methods Softw.} }
@STRING{optimmeth={Optim. Methods Softw.} }
@STRING{optimmeth={Optim. Methods Softw.} }
@STRING{oxfordpress={Oxford University Press} }
@STRING{oxfordpress={Oxford University Press} }
@STRING{oxfordpress={Oxford University Press} }
@STRING{pacificmath={Pacific J. Math.} }
@STRING{pacificmath={Pacific J. Math.} }
@STRING{pacificmath={Pacific J. Math.} }
@STRING{parcomputing={Parallel Comput.} }
@STRING{parcomputing={Parallel Comput.} }
@STRING{parcomputing={Parallel Comput.} }
@STRING{pardistcomp={J. Parallel and Distrib. Comput.} }
@STRING{pardistcomp={J. Parallel and Distrib. Comput.} }
@STRING{pardistcomp={J. Parallel and Distrib. Comput.} }
@STRING{pergamonpress={Pergamon Press} }
@STRING{pergamonpress={Pergamon Press} }
@STRING{pergamonpress={Pergamon Press} }
@STRING{philmag	= {Philos. Mag.} }
@STRING{philmag	= {Philos. Mag.} }
@STRING{philmag	= {Philos. Mag.} }
@STRING{plenumpress={Plenum Press} }
@STRING{plenumpress={Plenum Press} }
@STRING{plenumpress={Plenum Press} }
@STRING{prenticehall={Prentice-Hall} }
@STRING{prenticehall={Prentice-Hall} }
@STRING{prenticehall={Prentice-Hall} }
@STRING{procams	= {Proc. Amer. Math. Soc.} }
@STRING{procams	= {Proc. Amer. Math. Soc.} }
@STRING{procams	= {Proc. Amer. Math. Soc.} }
@STRING{procieee= {Proc. {IEEE}} }
@STRING{procieee= {Proc. {IEEE}} }
@STRING{procieee= {Proc. {IEEE}} }
@STRING{procnas	= {Proc.\@ Natl.\@ Acad.\@ Sci.\@ USA} }
@STRING{procnas	= {Proc.\@ Natl.\@ Acad.\@ Sci.\@ USA} }
@STRING{procnas	= {Proc.\@ Natl.\@ Acad.\@ Sci.\@ USA} }
@STRING{psychometrika={Psychometrika} }
@STRING{psychometrika={Psychometrika} }
@STRING{psychometrika={Psychometrika} }
@STRING{quartapplmath={Quart. Appl. Math.} }
@STRING{quartapplmath={Quart. Appl. Math.} }
@STRING{quartapplmath={Quart. Appl. Math.} }
@STRING{quartmath={Quart. J. Math. Oxford Ser. (2)} }
@STRING{quartmath={Quart. J. Math. Oxford Ser. (2)} }
@STRING{quartmath={Quart. J. Math. Oxford Ser. (2)} }
@STRING{revueinststat={Rev. Inst. Internat. Statist.} }
@STRING{revueinststat={Rev. Inst. Internat. Statist.} }
@STRING{revueinststat={Rev. Inst. Internat. Statist.} }
@STRING{scientific={The Scientific Press} }
@STRING{scientific={The Scientific Press} }
@STRING{scientific={The Scientific Press} }
@STRING{siamalgmeth={{SIAM} J. Algebraic Discrete Methods} }
@STRING{siamalgmeth={{SIAM} J. Algebraic Discrete Methods} }
@STRING{siamalgmeth={{SIAM} J. Algebraic Discrete Methods} }
@STRING{siamappmath={{SIAM} J. Appl. Math.} }
@STRING{siamappmath={{SIAM} J. Appl. Math.} }
@STRING{siamappmath={{SIAM} J. Appl. Math.} }
@STRING{siamcomp= {{SIAM} J. Comput.} }
@STRING{siamcomp= {{SIAM} J. Comput.} }
@STRING{siamcomp= {{SIAM} J. Comput.} }
@STRING{siamcontrol={{SIAM} J. Control Optim.} }
@STRING{siamcontrol={{SIAM} J. Control Optim.} }
@STRING{siamcontrol={{SIAM} J. Control Optim.} }
@STRING{siammath= {{SIAM} J. Math. Anal.} }
@STRING{siammath= {{SIAM} J. Math. Anal.} }
@STRING{siammath= {{SIAM} J. Math. Anal.} }
@STRING{siammatrix={{SIAM} J. Matrix Anal. Appl.} }
@STRING{siammatrix={{SIAM} J. Matrix Anal. Appl.} }
@STRING{siammatrix={{SIAM} J. Matrix Anal. Appl.} }
@STRING{siammms	= {Multiscale Model. Simul.} }
@STRING{siammms	= {Multiscale Model. Simul.} }
@STRING{siammms	= {Multiscale Model. Simul.} }
@STRING{siamnumanal={{SIAM} J. Numer. Anal.} }
@STRING{siamnumanal={{SIAM} J. Numer. Anal.} }
@STRING{siamnumanal={{SIAM} J. Numer. Anal.} }
@STRING{siamopt	= {{SIAM} J. Optim.} }
@STRING{siamopt	= {{SIAM} J. Optim.} }
@STRING{siamopt	= {{SIAM} J. Optim.} }
@STRING{siampub	= {Society of Industrial and Applied Mathematics} }
@STRING{siampub	= {Society of Industrial and Applied Mathematics} }
@STRING{siampub	= {Society of Industrial and Applied Mathematics} }
@STRING{siamreview={{SIAM} Rev.} }
@STRING{siamreview={{SIAM} Rev.} }
@STRING{siamreview={{SIAM} Rev.} }
@STRING{siamscicomp={{SIAM} J. Sci. Comput.} }
@STRING{siamscicomp={{SIAM} J. Sci. Comput.} }
@STRING{siamscicomp={{SIAM} J. Sci. Comput.} }
@STRING{siamscistatcomp={{SIAM} J. Sci. Stat. Comput.} }
@STRING{siamscistatcomp={{SIAM} J. Sci. Stat. Comput.} }
@STRING{siamsiims={{SIAM} J. Imag. Sci.} }
@STRING{siamsiims={{SIAM} J. Imag. Sci.} }
@STRING{siamsiims={{SIAM} J. Imag. Sci.} }
@STRING{signum	= {{ACM} {SIGNUM} Newslett.} }
@STRING{signum	= {{ACM} {SIGNUM} Newslett.} }
@STRING{signum	= {{ACM} {SIGNUM} Newslett.} }
@STRING{softpracexp={Software Prac. Experience} }
@STRING{softpracexp={Software Prac. Experience} }
@STRING{softpracexp={Software Prac. Experience} }
@STRING{springer= {Springer-Verlag} }
@STRING{springer= {Springer-Verlag} }
@STRING{springer= {Springer-Verlag} }
@STRING{stanford= {Stanford University Press} }
@STRING{stanford= {Stanford University Press} }
@STRING{stanford= {Stanford University Press} }
@STRING{statscience={Statist. Sci.} }
@STRING{statscience={Statist. Sci.} }
@STRING{statscience={Statist. Sci.} }
@STRING{tablesaidscomp={Math. Tables Aids Comput.} }
@STRING{tablesaidscomp={Math. Tables Aids Comput.} }
@STRING{tablesaidscomp={Math. Tables Aids Comput.} }
@STRING{techno	= {Technometrics} }
@STRING{techno	= {Technometrics} }
@STRING{techno	= {Technometrics} }
@STRING{texaspress={University of Texas Press} }
@STRING{texaspress={University of Texas Press} }
@STRING{texaspress={University of Texas Press} }
@STRING{transams= {Trans. Amer. Math. Soc.} }
@STRING{transams= {Trans. Amer. Math. Soc.} }
@STRING{transams= {Trans. Amer. Math. Soc.} }
@STRING{ussrcompmathphys={{U. S. S. R.} Comput. Math. and Math. Phys.} }
@STRING{ussrcompmathphys={{U. S. S. R.} Comput. Math. and Math. Phys.} }
@STRING{ussrcompmathphys={{U. S. S. R.} Comput. Math. and Math. Phys.} }
@STRING{vannostrand={Van Nostrand} }
@STRING{vannostrand={Van Nostrand} }
@STRING{vannostrand={Van Nostrand} }
@STRING{vlsicompsys={J. {VLSI} Comput. Syst.} }
@STRING{vlsicompsys={J. {VLSI} Comput. Syst.} }
@STRING{vlsicompsys={J. {VLSI} Comput. Syst.} }
@STRING{whfreeman={W. H. Freeman and Co.} }
@STRING{whfreeman={W. H. Freeman and Co.} }
@STRING{whfreeman={W. H. Freeman and Co.} }
@STRING{zangewmathmech={Z. Angew. Math. Mech.} }
@STRING{zangewmathmech={Z. Angew. Math. Mech.} }
@STRING{zangewmathmech={Z. Angew. Math. Mech.} }
@STRING{zangewmathphys={Z. Angew. Math. Phys.} }
@STRING{zangewmathphys={Z. Angew. Math. Phys.} }
@STRING{zangewmathphys={Z. Angew. Math. Phys.} }

@Article{	  10.1137/070697835,
  title		= {Guaranteed Minimum-Rank Solutions of Linear Matrix
		  Equations via Nuclear Norm Minimization},
  year		= 2010,
  doi		= {DOI:10.1137/070697835},
  issn		= 00361445,
  eissn		= 10957200,
  journal	= siamreview,
  volume	= 52,
  number	= 3,
  pages		= {471-501},
  author	= {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
  keywords	= {rank; convex optimization; matrix norms; random matrices;
		  compressed sensing; semidefinite programming; 90C25; 90C59;
		  15A52; },
  url		= {http://dx.doi.org/doi/10.1137/070697835}
}

@Misc{		  1401.4220,
  author	= {Sahar Karimi and Stephen Vavasis},
  title		= {{IMRO}: a proximal quasi-{Newton} method for solving
		  $l_1$-regularized least squares problem},
  year		= 2014,
  eprint	= {arXiv:1401.4220}
}

###Misc{	  1401.4220,
  author	= {Sahar Karimi and Stephen Vavasis},
  title		= {{IMRO}: a proximal quasi-{Newton} method for solving
		  $l_1$-regularized least square problem},
  year		= 2014,
  eprint	= {arXiv:1401.4220}
}

@Article{	  1993,
  title		= {Lagrange Multipliers and Optimality},
  author	= {Rockafellar, R. T.},
  journal	= {SIAM Review},
  volume	= 35,
  number	= 2,
  pages		= {pp. 183-238},
  url		= {http://www.jstor.org/stable/2133143},
  issn		= 00361445,
  abstract	= {Lagrange multipliers used to be viewed as auxiliary
		  variables introduced in a problem of constrained
		  minimization in order to write first-order optimality
		  conditions formally as a system of equations. Modern
		  applications, with their emphasis on numerical methods and
		  more complicated side conditions than equations, have
		  demanded deeper understanding of the concept and how it
		  fits into a larger theoretical picture. A major line of
		  research has been the nonsmooth geometry of one-sided
		  tangent and normal vectors to the set of points satisfying
		  the given constraints. Another has been the game-theoretic
		  role of multiplier vectors as solutions to a dual problem.
		  Interpretations as generalized derivatives of the optimal
		  value with respect to problem parameters have also been
		  explored. Lagrange multipliers are now being seen as
		  arising from a general rule for the subdifferentiation of a
		  nonsmooth objective function which allows black-and-white
		  constraints to be replaced by penalty expressions. This
		  paper traces such themes in the current theory of Lagrange
		  multipliers, providing along the way a free-standing
		  exposition of basic nonsmooth analysis as motivated by and
		  applied to this subject.},
  year		= 1993
}

###Article{	  1993,
  title		= {Lagrange Multipliers and Optimality},
  author	= {Rockafellar, R. T.},
  journal	= {SIAM Review},
  volume	= 35,
  number	= 2,
  pages		= {pp. 183-238},
  url		= {http://www.jstor.org/stable/2133143},
  abstract	= {Lagrange multipliers used to be viewed as auxiliary
		  variables introduced in a problem of constrained
		  minimization in order to write first-order optimality
		  conditions formally as a system of equations. Modern
		  applications, with their emphasis on numerical methods and
		  more complicated side conditions than equations, have
		  demanded deeper understanding of the concept and how it
		  fits into a larger theoretical picture. A major line of
		  research has been the nonsmooth geometry of one-sided
		  tangent and normal vectors to the set of points satisfying
		  the given constraints. Another has been the game-theoretic
		  role of multiplier vectors as solutions to a dual problem.
		  Interpretations as generalized derivatives of the optimal
		  value with respect to problem parameters have also been
		  explored. Lagrange multipliers are now being seen as
		  arising from a general rule for the subdifferentiation of a
		  nonsmooth objective function which allows black-and-white
		  constraints to be replaced by penalty expressions. This
		  paper traces such themes in the current theory of Lagrange
		  multipliers, providing along the way a free-standing
		  exposition of basic nonsmooth analysis as motivated by and
		  applied to this subject.},
  year		= 1993
}

###Article{	  1993,
  title		= {Lagrange Multipliers and Optimality},
  author	= {Rockafellar, R. T.},
  journal	= {SIAM Review},
  volume	= 35,
  number	= 2,
  pages		= {pp. 183-238},
  url		= {http://www.jstor.org/stable/2133143},
  abstract	= {Lagrange multipliers used to be viewed as auxiliary
		  variables introduced in a problem of constrained
		  minimization in order to write first-order optimality
		  conditions formally as a system of equations. Modern
		  applications, with their emphasis on numerical methods and
		  more complicated side conditions than equations, have
		  demanded deeper understanding of the concept and how it
		  fits into a larger theoretical picture. A major line of
		  research has been the nonsmooth geometry of one-sided
		  tangent and normal vectors to the set of points satisfying
		  the given constraints. Another has been the game-theoretic
		  role of multiplier vectors as solutions to a dual problem.
		  Interpretations as generalized derivatives of the optimal
		  value with respect to problem parameters have also been
		  explored. Lagrange multipliers are now being seen as
		  arising from a general rule for the subdifferentiation of a
		  nonsmooth objective function which allows black-and-white
		  constraints to be replaced by penalty expressions. This
		  paper traces such themes in the current theory of Lagrange
		  multipliers, providing along the way a free-standing
		  exposition of basic nonsmooth analysis as motivated by and
		  applied to this subject.},
  year		= 1993
}

@Article{	  6399612,
  author	= {Lorenz, D. A.},
  journal	= ieeetranssigproc,
  title		= {Constructing Test Instances for Basis Pursuit Denoising},
  year		= 2013,
  volume	= 61,
  number	= 5,
  pages		= {1210-1214},
  keywords	= {signal denoising;signal reconstruction;LASSO-problem;basis
		  pursuit denoising problem;source condition;sparse
		  regularization;Dynamic range;Inverse
		  problems;Minimization;Noise;Noise reduction;Sparse
		  matrices;Vectors;Basis Pursuit denoising;compressed
		  sensing;optimization;optimization algorithms;signal
		  processing algorithms;sparsity},
  month		= {March}
}

###Article{	  6399612,
  author	= {Lorenz, D. A.},
  journal	= ieeetranssigproc,
  title		= {Constructing Test Instances for Basis Pursuit Denoising},
  year		= 2013,
  volume	= 61,
  number	= 5,
  pages		= {1210-1214},
  keywords	= {signal denoising;signal reconstruction;LASSO-problem;basis
		  pursuit denoising problem;source condition;sparse
		  regularization;Dynamic range;Inverse
		  problems;Minimization;Noise;Noise reduction;Sparse
		  matrices;Vectors;Basis Pursuit denoising;compressed
		  sensing;optimization;optimization algorithms;signal
		  processing algorithms;sparsity},
  month		= {March}
}

@InBook{	  abad:1967,
  address	= {Amsterdam},
  author	= {J. Abadie},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. Abadie},
  pages		= {19--36},
  publisher	= northholland,
  title		= {Nonlinear Programming},
  year		= 1967
}

###InBook{	  abad:1967,
  address	= {Amsterdam},
  author	= {J. Abadie},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. Abadie},
  pages		= {19--36},
  publisher	= northholland,
  title		= {Nonlinear Programming},
  year		= 1967
}

###InBook{	  abad:1967,
  address	= {Amsterdam},
  author	= {J. Abadie},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. Abadie},
  pages		= {19--36},
  publisher	= northholland,
  title		= {Nonlinear Programming},
  year		= 1967
}

@Article{	  abev09,
  author	= "Jacob Abernethy and Francis Bach and Theodoros Evgeniou
		  and Jean-Philippe Vert",
  title		= "A new approach to collaborative filtering: operator
		  estimation with spectral regularization",
  journal	= "Journal of Machine Learning Research",
  year		= 2009
}

###Article{	  abev09,
  author	= "Jacob Abernethy and Francis Bach and Theodoros Evgeniou
		  and Jean-Philippe Vert",
  title		= "A new approach to collaborative filtering: operator
		  estimation with spectral regularization",
  journal	= "Journal of Machine Learning Research",
  year		= 2009
}

###Article{	  abev09,
  author	= "Jacob Abernethy and Francis Bach and Theodoros Evgeniou
		  and Jean-Philippe Vert",
  title		= "A new approach to collaborative filtering: operator
		  estimation with spectral regularization",
  journal	= "Journal of Machine Learning Research",
  year		= 2009
}

@Article{	  aep08,
  author	= "Andreas Argyriou and Theodoros Evgeniou and Massimiliano
		  Pontil",
  title		= "Convex multi-task feature learning",
  journal	= "Machine Learning",
  year		= 2008,
  volume	= 73,
  page		= {243--272}
}

###Article{	  aep08,
  author	= "Andreas Argyriou and Theodoros Evgeniou and Massimiliano
		  Pontil",
  title		= "Convex multi-task feature learning",
  journal	= "Machine Learning",
  year		= 2008,
  volume	= 73,
  page		= {243--272}
}

###Article{	  aep08,
  author	= "Andreas Argyriou and Theodoros Evgeniou and Massimiliano
		  Pontil",
  title		= "Convex multi-task feature learning",
  journal	= "Machine Learning",
  year		= 2008,
  volume	= 73,
  page		= {243--272}
}

@InProceedings{	  agy04,
  title		= "On the computational complexity of sensor network
		  localization",
  author	= "James Aspnes and David Goldenberg and Yang Richard Yang",
  booktitle	= "Algorithmic Aspects of Wireless Sensor Networks: First
		  International Workshop, ALGOSENSORS 2004",
  address	= {Turku, Finland},
  series	= {Lecture Notes in Computer Science},
  volume	= 3121,
  publisher	= "Springer-Verlag",
  year		= 2004,
  month		= {July},
  pages		= {32--44}
}

###InProceedings{ agy04,
  title		= "On the computational complexity of sensor network
		  localization",
  author	= "James Aspnes and David Goldenberg and Yang Richard Yang",
  booktitle	= "Algorithmic Aspects of Wireless Sensor Networks: First
		  International Workshop, ALGOSENSORS 2004",
  address	= {Turku, Finland},
  series	= {Lecture Notes in Computer Science},
  volume	= 3121,
  publisher	= "Springer-Verlag",
  year		= 2004,
  month		= {July},
  pages		= {32--44}
}

###InProceedings{ agy04,
  title		= "On the computational complexity of sensor network
		  localization",
  author	= "James Aspnes and David Goldenberg and Yang Richard Yang",
  booktitle	= "Algorithmic Aspects of Wireless Sensor Networks: First
		  International Workshop, ALGOSENSORS 2004",
  address	= {Turku, Finland},
  series	= {Lecture Notes in Computer Science},
  volume	= 3121,
  publisher	= "Springer-Verlag",
  year		= 2004,
  month		= {July},
  pages		= {32--44}
}

@Article{	  alf00,
  author	= "A. Y. Alfakih",
  title		= "Graph rigidity via {Euclidean} distance matrices",
  journal	= "Linear Algebra and its Applications",
  year		= 2000,
  volume	= 310,
  page		= {149--165}
}

###Article{	  alf00,
  author	= "A. Y. Alfakih",
  title		= "Graph rigidity via {Euclidean} distance matrices",
  journal	= "Linear Algebra and its Applications",
  year		= 2000,
  volume	= 310,
  page		= {149--165}
}

###Article{	  alf00,
  author	= "A. Y. Alfakih",
  title		= "Graph rigidity via {Euclidean} distance matrices",
  journal	= "Linear Algebra and its Applications",
  year		= 2000,
  volume	= 310,
  page		= {149--165}
}

@Misc{		  alipanahi:2012,
  title		= {Determining Protein Structures from {NOESY} Distance
		  Constraints by Semidefinite Programming},
  author	= {Babak Alipanahi and Nathan Krislock and Ali Ghodsi and
		  Henry Wolkowicz and Logan Donaldson and Ming Li},
  year		= 2012,
  note		= {To appear in {\it Journal of Computational Biology}}
}

###Misc{	  alipanahi:2012,
  title		= {Determining Protein Structures from {NOESY} Distance
		  Constraints by Semidefinite Programming},
  author	= {Babak Alipanahi and Nathan Krislock and Ali Ghodsi and
		  Henry Wolkowicz and Logan Donaldson and Ming Li},
  year		= 2012,
  note		= {To appear in {\it Journal of Computational Biology}}
}

###Misc{	  alipanahi:2012,
  title		= {Determining Protein Structures from {NOESY} Distance
		  Constraints by Semidefinite Programming},
  author	= {Babak Alipanahi and Nathan Krislock and Ali Ghodsi and
		  Henry Wolkowicz and Logan Donaldson and Ming Li},
  year		= 2012,
  note		= {To appear in {\it Journal of Computational Biology}}
}

@InCollection{	  alizadehschmieta:2000,
  author	= {Farid Alizadeh and Stefan Schmieta},
  title		= {Symmetric Cones, Potential Reduction Methods},
  booktitle	= {Handbook of semidefinite programming: theory, algorithms,
		  and applications},
  publisher	= {Springer},
  year		= 2000,
  editor	= {Wolkowicz, Henry and Saigal, Romesh and Vandenberghe,
		  Lieven},
  volume	= 27
}

###InCollection{  alizadehschmieta:2000,
  author	= {Farid Alizadeh and Stefan Schmieta},
  title		= {Symmetric Cones, Potential Reduction Methods},
  booktitle	= {Handbook of semidefinite programming: theory, algorithms,
		  and applications},
  publisher	= {Springer},
  year		= 2000,
  editor	= {Wolkowicz, Henry and Saigal, Romesh and Vandenberghe,
		  Lieven},
  volume	= 27
}

@Article{	  alizgold:2003,
  author	= {F. Alizadeh and D. Goldfarb},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathprogb,
  pages		= {3--51},
  title		= {Second-order cone programming},
  volume	= 95,
  year		= 2003
}

###Article{	  alizgold:2003,
  author	= {F. Alizadeh and D. Goldfarb},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathprogb,
  pages		= {3--51},
  title		= {Second-order cone programming},
  volume	= 95,
  year		= 2003
}

###Article{	  alizgold:2003,
  author	= {F. Alizadeh and D. Goldfarb},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathprogb,
  pages		= {3--51},
  title		= {Second-order cone programming},
  volume	= 95,
  year		= 2003
}

@Article{	  altmgond:1999,
  author	= {A. Altman and J. Gondzio},
  date-modified	= {2007-09-07 10:48:13 -0700},
  journal	= {Optim. Methods Softw.},
  local-url	= {file://localhost/Users/mpf/papers/AltmanGondzio99.pdf},
  pages		= {275--302},
  title		= {Regularized Symmetric Indefinite Systems in Interior Point
		  Methods for Linear and Quadratic Optimization},
  volume	= 11,
  year		= 1999
}

###Article{	  altmgond:1999,
  author	= {A. Altman and J. Gondzio},
  date-modified	= {2007-09-07 10:48:13 -0700},
  journal	= {Optim. Methods Softw.},
  pages		= {275--302},
  title		= {Regularized Symmetric Indefinite Systems in Interior Point
		  Methods for Linear and Quadratic Optimization},
  volume	= 11,
  year		= 1999
}

###Article{	  altmgond:1999,
  author	= {A. Altman and J. Gondzio},
  date-modified	= {2007-09-07 10:48:13 -0700},
  journal	= {Optim. Methods Softw.},
  pages		= {275--302},
  title		= {Regularized Symmetric Indefinite Systems in Interior Point
		  Methods for Linear and Quadratic Optimization},
  volume	= 11,
  year		= 1999
}

@Article{	  ames2011nuclear,
  title		= {Nuclear norm minimization for the planted clique and
		  biclique problems},
  author	= {Ames, Brendan PW and Vavasis, Stephen A},
  journal	= mathprog,
  volume	= 129,
  number	= 1,
  pages		= {69--89},
  year		= 2011,
  publisher	= {Springer}
}

###Article{	  ames2011nuclear,
  title		= {Nuclear norm minimization for the planted clique and
		  biclique problems},
  author	= {Ames, Brendan PW and Vavasis, Stephen A},
  journal	= mathprog,
  volume	= 129,
  number	= 1,
  pages		= {69--89},
  year		= 2011,
  publisher	= {Springer}
}

@Article{	  ande:1996,
  author	= {Andersen, Knud D.},
  date-modified	= {2007-07-18 14:59:32 -0700},
  fjournal	= siamopt,
  issn		= {1052-6234},
  journal	= siamopt,
  number	= 1,
  pages		= {74--95},
  title		= {An efficient {N}ewton barrier method for minimizing a sum
		  of {E}uclidean norms},
  volume	= 6,
  year		= 1996
}

###Article{	  ande:1996,
  author	= {Andersen, Knud D.},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {74--95},
  title		= {An efficient {N}ewton barrier method for minimizing a sum
		  of {E}uclidean norms},
  volume	= 6,
  year		= 1996
}

###Article{	  ande:1996,
  author	= {Andersen, Knud D.},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {74--95},
  title		= {An efficient {N}ewton barrier method for minimizing a sum
		  of {E}uclidean norms},
  volume	= 6,
  year		= 1996
}

@Article{	  andechriconnover:2000,
  author	= {Andersen, Knud D. and Christiansen, Edmund and Conn,
		  Andrew R. and Overton, M. L.},
  date-modified	= {2007-07-18 14:59:32 -0700},
  issn		= {1064-8275},
  journal	= siamscicomp,
  number	= 1,
  pages		= {243--262},
  title		= {An efficient primal-dual interior-point method for
		  minimizing a sum of {E}uclidean norms},
  volume	= 22,
  year		= 2000
}

###Article{	  andechriconnover:2000,
  author	= {Andersen, Knud D. and Christiansen, Edmund and Conn,
		  Andrew R. and Overton, M. L.},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamscicomp,
  number	= 1,
  pages		= {243--262},
  title		= {An efficient primal-dual interior-point method for
		  minimizing a sum of {E}uclidean norms},
  volume	= 22,
  year		= 2000
}

###Article{	  andechriconnover:2000,
  author	= {Andersen, Knud D. and Christiansen, Edmund and Conn,
		  Andrew R. and Overton, M. L.},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamscicomp,
  number	= 1,
  pages		= {243--262},
  title		= {An efficient primal-dual interior-point method for
		  minimizing a sum of {E}uclidean norms},
  volume	= 22,
  year		= 2000
}

@Article{	  andeelfv:1997,
  author	= {Lars-Erik Andersson and Tommy Elfving},
  title		= {A Constrained Procrustes Problem},
  publisher	= {SIAM},
  year		= 1997,
  journal	= siammatrix,
  volume	= 18,
  number	= 1,
  pages		= {124-139},
  keywords	= {constrained matrices; convex cones; positive semidefinite;
		  Procrustes},
  url		= {http://link.aip.org/link/?SML/18/124/1},
  doi		= {10.1137/S0895479894277545}
}

###Article{	  andeelfv:1997,
  author	= {Lars-Erik Andersson and Tommy Elfving},
  title		= {A Constrained Procrustes Problem},
  publisher	= {SIAM},
  year		= 1997,
  journal	= siammatrix,
  volume	= 18,
  number	= 1,
  pages		= {124-139},
  keywords	= {constrained matrices; convex cones; positive semidefinite;
		  Procrustes},
  url		= {http://link.aip.org/link/?SML/18/124/1},
  doi		= {10.1137/S0895479894277545}
}

###Article{	  andeelfv:1997,
  author	= {Lars-Erik Andersson and Tommy Elfving},
  title		= {A Constrained Procrustes Problem},
  publisher	= {SIAM},
  year		= 1997,
  journal	= siammatrix,
  volume	= 18,
  number	= 1,
  pages		= {124-139},
  keywords	= {constrained matrices; convex cones; positive semidefinite;
		  Procrustes},
  url		= {http://link.aip.org/link/?SML/18/124/1},
  doi		= {10.1137/S0895479894277545}
}

@Article{	  anderoosterl:2003,
  author	= {E. D. Anderson and C. Roos and T. Terlaky},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogb,
  pages		= {249--277},
  title		= {On implementing a primal-dual interior-point method for
		  conic quadratic optimization},
  volume	= 95,
  year		= 2003
}

###Article{	  anderoosterl:2003,
  author	= {E. D. Anderson and C. Roos and T. Terlaky},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogb,
  pages		= {249--277},
  title		= {On implementing a primal-dual interior-point method for
		  conic quadratic optimization},
  volume	= 95,
  year		= 2003
}

###Article{	  anderoosterl:2003,
  author	= {E. D. Anderson and C. Roos and T. Terlaky},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogb,
  pages		= {249--277},
  title		= {On implementing a primal-dual interior-point method for
		  conic quadratic optimization},
  volume	= 95,
  year		= 2003
}

@Article{	  andersen:2010,
  year		= 2010,
  journal	= mathprogc,
  volume	= 2,
  number	= {3-4},
  title		= {Implementation of nonsymmetric interior-point methods for
		  linear optimization over sparse matrix cones},
  publisher	= {Springer-Verlag},
  keywords	= {90-08 Mathematical Programming - computational methods;
		  90C06 Mathematical Programming - large-scale; 90C22
		  Mathematical Programming - semidefinite programing; 90C25
		  Mathematical Programming - convex programming; 90C51
		  Mathematical Programming - interior-point methods},
  author	= {Andersen, MartinS. and Dahl, Joachim and Vandenberghe,
		  Lieven},
  pages		= {167-201},
  language	= {English}
}

###Article{	  andersen:2010,
  year		= 2010,
  journal	= {Mathematical Programming Computation},
  volume	= 2,
  number	= {3-4},
  title		= {Implementation of nonsymmetric interior-point methods for
		  linear optimization over sparse matrix cones},
  publisher	= {Springer-Verlag},
  keywords	= {90-08 Mathematical Programming - computational methods;
		  90C06 Mathematical Programming - large-scale; 90C22
		  Mathematical Programming - semidefinite programing; 90C25
		  Mathematical Programming - convex programming; 90C51
		  Mathematical Programming - interior-point methods},
  author	= {Andersen, MartinS. and Dahl, Joachim and Vandenberghe,
		  Lieven},
  pages		= {167-201},
  language	= {English}
}

@Article{	  andrbirgmartschu:2006,
  abstract	= {Two Augmented Lagrangian algorithms for solving KKT
		  systems are introduced. The algorithms differ in the way in
		  which penalty parameters are updated. Possibly infeasible
		  accumulation points are characterized. It is proved that
		  feasible limit points that satisfy the Constant Positive
		  Linear Dependence constraint qualification are KKT
		  solutions. Boundedness of the penalty parameters is proved
		  under suitable assumptions. Numerical experiments are
		  presented.},
  annote	= {Published online.},
  author	= {R. Andreani and E. G. Birgin and J. M. Mart\'{i}nez and M.
		  L. Schuverdt},
  journal	= {Math. Program. B},
  local-url	= {file://localhost/Users/mpf/papers/AndrBirgMartSchu06.pdf},
  month		= {December},
  title		= {Augmented Lagrangian methods under the constant positive
		  linear dependence constraint qualification},
  year		= 2008,
  pages		= {5--32},
  volume	= 111,
  numbers	= {1--2}
}

###Article{	  andrbirgmartschu:2006,
  abstract	= {Two Augmented Lagrangian algorithms for solving KKT
		  systems are introduced. The algorithms differ in the way in
		  which penalty parameters are updated. Possibly infeasible
		  accumulation points are characterized. It is proved that
		  feasible limit points that satisfy the Constant Positive
		  Linear Dependence constraint qualification are KKT
		  solutions. Boundedness of the penalty parameters is proved
		  under suitable assumptions. Numerical experiments are
		  presented.},
  annote	= {Published online.},
  author	= {R. Andreani and E. G. Birgin and J. M. Mart\'{i}nez and M.
		  L. Schuverdt},
  journal	= {Math. Program. B},
  local-url	= {file://localhost/Users/mpf/papers/AndrBirgMartSchu06.pdf},
  month		= {December},
  title		= {Augmented Lagrangian methods under the constant positive
		  linear dependence constraint qualification},
  year		= 2008,
  pages		= {5--32},
  volume	= 111,
  numbers	= {1--2}
}

###Article{	  andrbirgmartschu:2006,
  abstract	= {Two Augmented Lagrangian algorithms for solving KKT
		  systems are introduced. The algorithms differ in the way in
		  which penalty parameters are updated. Possibly infeasible
		  accumulation points are characterized. It is proved that
		  feasible limit points that satisfy the Constant Positive
		  Linear Dependence constraint qualification are KKT
		  solutions. Boundedness of the penalty parameters is proved
		  under suitable assumptions. Numerical experiments are
		  presented.},
  annote	= {Published online.},
  author	= {R. Andreani and E. G. Birgin and J. M. Mart\'{i}nez and M.
		  L. Schuverdt},
  journal	= {Math. Program. B},
  local-url	= {file://localhost/Users/mpf/papers/AndrBirgMartSchu06.pdf},
  month		= {December},
  title		= {Augmented Lagrangian methods under the constant positive
		  linear dependence constraint qualification},
  year		= 2008,
  pages		= {5--32},
  volume	= 111,
  numbers	= {1--2}
}

@TechReport{	  anit:2000,
  author	= {Mihai Anitescu},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Argonne National Laboratory},
  number	= {ANL/MCS-P864-1200},
  title		= {On solving mathematical programs with complementarity
		  constraints as nonlinear programs},
  year		= 2000
}

###TechReport{	  anit:2000,
  author	= {Mihai Anitescu},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Argonne National Laboratory},
  number	= {ANL/MCS-P864-1200},
  title		= {On solving mathematical programs with complementarity
		  constraints as nonlinear programs},
  year		= 2000
}

###TechReport{	  anit:2000,
  author	= {Mihai Anitescu},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Argonne National Laboratory},
  number	= {ANL/MCS-P864-1200},
  title		= {On solving mathematical programs with complementarity
		  constraints as nonlinear programs},
  year		= 2000
}

@Article{	  anit:2006,
  author	= {M. Anitescu},
  title		= {Optimization-based simulation of nonsmooth rigid multibody
		  dynamics},
  journal	= mathprog,
  year		= 2006,
  volume	= 105,
  number	= 1,
  pages		= {113--143}
}

###Article{	  anit:2006,
  author	= {M. Anitescu},
  title		= {Optimization-based simulation of nonsmooth rigid multibody
		  dynamics},
  journal	= mathprog,
  year		= 2006,
  volume	= 105,
  number	= 1,
  pages		= {113--143}
}

###Article{	  anit:2006,
  author	= {M. Anitescu},
  title		= {Optimization-based simulation of nonsmooth rigid multibody
		  dynamics},
  journal	= mathprog,
  year		= 2006,
  volume	= 105,
  number	= 1,
  pages		= {113--143}
}

@Article{	  aravkin2013sparse,
  title		= {Sparse/robust estimation and {Kalman} smoothing with
		  nonsmooth log-concave densities: Modeling, computation, and
		  theory},
  author	= {Aravkin, Aleksandr Y and Burke, James V and Pillonetto,
		  Gianluigi},
  journal	= {J. Mach. Learn. Res.},
  volume	= 14,
  number	= 1,
  pages		= {2689--2728},
  year		= 2013,
  publisher	= {JMLR.org}
}

###Article{	  aravkin2013sparse,
  title		= {Sparse/robust estimation and Kalman smoothing with
		  nonsmooth log-concave densities: Modeling, computation, and
		  theory},
  author	= {Aravkin, Aleksandr Y and Burke, James V and Pillonetto,
		  Gianluigi},
  journal	= {J. Mach. Learn. Res.},
  volume	= 14,
  number	= 1,
  pages		= {2689--2728},
  year		= 2013,
  publisher	= {JMLR.org}
}

@Article{	  aravkinleeuwen:2012,
  author	= {Aleksandr Y Aravkin and Tristan van Leeuwen},
  title		= {Estimating nuisance parameters in inverse problems},
  journal	= {Inverse Probl.},
  volume	= 28,
  number	= 11,
  pages		= 115016,
  url		= {http://stacks.iop.org/0266-5611/28/i=11/a=115016},
  year		= 2012,
  abstract	= {Many inverse problems include nuisance parameters which,
		  while not of direct interest, are required to recover
		  primary parameters. The structure of these problems allows
		  efficient optimization strategies—a well-known example is
		  variable projection , where nonlinear least-squares
		  problems which are linear in some parameters can be very
		  efficiently optimized. In this paper, we extend the idea of
		  projecting out a subset over the variables to a broad class
		  of maximum likelihood and maximum a posteriori likelihood
		  problems with nuisance parameters, such as variance or
		  degrees of freedom (d.o.f.). As a result, we are able to
		  incorporate nuisance parameter estimation into large-scale
		  constrained and unconstrained inverse problem formulations.
		  We apply the approach to a variety of problems, including
		  estimation of unknown variance parameters in the Gaussian
		  model, d.o.f. parameter estimation in the context of robust
		  inverse problems, and automatic calibration. Using
		  numerical examples, we demonstrate improvement in recovery
		  of primary parameters for several large-scale inverse
		  problems. The proposed approach is compatible with a wide
		  variety of algorithms and formulations, and its
		  implementation requires only minor modifications to
		  existing algorithms.}
}

###Article{	  aravkinleeuwen:2012,
  author	= {Aleksandr Y Aravkin and Tristan van Leeuwen},
  title		= {Estimating nuisance parameters in inverse problems},
  journal	= {Inverse Probl.},
  volume	= 28,
  number	= 11,
  pages		= 115016,
  url		= {http://stacks.iop.org/0266-5611/28/i=11/a=115016},
  year		= 2012,
  abstract	= {Many inverse problems include nuisance parameters which,
		  while not of direct interest, are required to recover
		  primary parameters. The structure of these problems allows
		  efficient optimization strategies—a well-known example is
		  variable projection , where nonlinear least-squares
		  problems which are linear in some parameters can be very
		  efficiently optimized. In this paper, we extend the idea of
		  projecting out a subset over the variables to a broad class
		  of maximum likelihood and maximum a posteriori likelihood
		  problems with nuisance parameters, such as variance or
		  degrees of freedom (d.o.f.). As a result, we are able to
		  incorporate nuisance parameter estimation into large-scale
		  constrained and unconstrained inverse problem formulations.
		  We apply the approach to a variety of problems, including
		  estimation of unknown variance parameters in the Gaussian
		  model, d.o.f. parameter estimation in the context of robust
		  inverse problems, and automatic calibration. Using
		  numerical examples, we demonstrate improvement in recovery
		  of primary parameters for several large-scale inverse
		  problems. The proposed approach is compatible with a wide
		  variety of algorithms and formulations, and its
		  implementation requires only minor modifications to
		  existing algorithms.}
}

@Article{	  armandbenois:2013,
  year		= 2013,
  journal	= mathprog,
  volume	= 137,
  number	= {1-2},
  doi		= {10.1007/s10107-011-0498-3},
  title		= {Uniform boundedness of the inverse of a Jacobian matrix
		  arising in regularized interior-point methods},
  url		= {http://dx.doi.org/10.1007/s10107-011-0498-3},
  publisher	= {Springer-Verlag},
  keywords	= {Constrained optimization; Primal-dual interior-point
		  method; 49M37; 65F05; 65F22; 65K05; 90C05; 90C30; 90C51},
  author	= {Armand, Paul and Benoist, Joël},
  pages		= {587-592},
  language	= {English}
}

###Article{	  armandbenois:2013,
  year		= 2013,
  journal	= mathprog,
  volume	= 137,
  number	= {1-2},
  doi		= {10.1007/s10107-011-0498-3},
  title		= {Uniform boundedness of the inverse of a Jacobian matrix
		  arising in regularized interior-point methods},
  url		= {http://dx.doi.org/10.1007/s10107-011-0498-3},
  publisher	= {Springer-Verlag},
  keywords	= {Constrained optimization; Primal-dual interior-point
		  method; 49M37; 65F05; 65F22; 65K05; 90C05; 90C30; 90C51},
  author	= {Armand, Paul and Benoist, Joël},
  pages		= {587-592},
  language	= {English}
}

@InCollection{	  arrosolo:1958,
  address	= {Stanford, CA},
  author	= {Kenneth J. Arrow and Robert M. Solow},
  booktitle	= {Studies in Linear and Nonlinear Programming},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {Kenneth J. Arrow and Leonid Hurwicz and Hirofumi Uzawa},
  pages		= {166--176},
  publisher	= stanford,
  title		= {Gradient methods for constrained maxima, with weakened
		  assumptions},
  year		= 1958
}

###InCollection{  arrosolo:1958,
  address	= {Stanford, CA},
  author	= {Kenneth J. Arrow and Robert M. Solow},
  booktitle	= {Studies in Linear and Nonlinear Programming},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {Kenneth J. Arrow and Leonid Hurwicz and Hirofumi Uzawa},
  pages		= {166--176},
  publisher	= stanford,
  title		= {Gradient methods for constrained maxima, with weakened
		  assumptions},
  year		= 1958
}

###InCollection{  arrosolo:1958,
  address	= {Stanford, CA},
  author	= {Kenneth J. Arrow and Robert M. Solow},
  booktitle	= {Studies in Linear and Nonlinear Programming},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {Kenneth J. Arrow and Leonid Hurwicz and Hirofumi Uzawa},
  pages		= {166--176},
  publisher	= stanford,
  title		= {Gradient methods for constrained maxima, with weakened
		  assumptions},
  year		= 1958
}

@Article{	  aus78,
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  editor	= "O. L. Mangazarian and R. R. Meyer and S. M. Robinson",
  title		= "Minimisation sans contraintes de fonctions localement
		  lipschitziennes: Applications \`a la programmation
		  mi-convexe, mi-diff\'erentiable",
  journal	= "Nonlinear Programming",
  year		= 1978,
  volume	= 3,
  pages		= {429--460},
  publisher	= "Academic press",
  address	= "New York"
}

###Article{	  aus78,
  author	= "A. Auslender",
  editor	= "O. L. Mangazarian and R. R. Meyer and S. M. Robinson",
  title		= "Minimisation sans contraintes de fonctions localement
		  lipschitziennes: Applications \`a la programmation
		  mi-convexe, mi-diff\'erentiable",
  journal	= "Nonlinear Programming",
  year		= 1978,
  volume	= 3,
  pages		= {429--460},
  publisher	= "Academic press",
  address	= "New York"
}

###Article{	  aus78,
  author	= "A. Auslender",
  editor	= "O. L. Mangazarian and R. R. Meyer and S. M. Robinson",
  title		= "Minimisation sans contraintes de fonctions localement
		  lipschitziennes: Applications \`a la programmation
		  mi-convexe, mi-diff\'erentiable",
  journal	= "Nonlinear Programming",
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  volume	= 3,
  pages		= {429--460},
  publisher	= "Academic press",
  address	= "New York"
}

@Article{	  aut05,
  author	= "A. Auslender and M. Teboulle",
  title		= "Interior projection-like methods for monotone variational
		  inequalities",
  journal	= "Mathematical Programming",
  year		= 2005,
  volume	= 104,
  page		= {39--68}
}

###Article{	  aut05,
  author	= "A. Auslender and M. Teboulle",
  title		= "Interior projection-like methods for monotone variational
		  inequalities",
  journal	= mathprog,
  year		= 2005,
  volume	= 104,
  page		= {39--68}
}

###Article{	  aut05,
  author	= "A. Auslender and M. Teboulle",
  title		= "Interior projection-like methods for monotone variational
		  inequalities",
  journal	= "Mathematical Programming",
  year		= 2005,
  volume	= 104,
  page		= {39--68}
}

@Article{	  aut06,
  author	= "A. Auslender and M. Teboulle",
  title		= "Interior Gradient and Proximal Methods for Convex and
		  Conic Optimization",
  journal	= siamopt,
  year		= 2006,
  volume	= 16,
  page		= {697--725}
}

###Article{	  aut06,
  author	= "A. Auslender and M. Teboulle",
  title		= "Interior Gradient and Proximal Methods for Convex and
		  Conic Optimization",
  journal	= siamopt,
  year		= 2006,
  volume	= 16,
  page		= {697--725}
}

###Article{	  aut06,
  author	= "A. Auslender and M. Teboulle",
  title		= "Interior Gradient and Proximal Methods for Convex and
		  Conic Optimization",
  journal	= siamopt,
  year		= 2006,
  volume	= 16,
  page		= {697--725}
}

@Article{	  avelinovicente:2003,
  author	= {C. P. Avelino and L. N. Vicente},
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		  Constraints in an Augmented Lagrangian Multiplier Method},
  journal	= jota,
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  pages		= {215--233}
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###Article{	  avelinovicente:2003,
  author	= {C. P. Avelino and L. N. Vicente},
  title		= {Updating the Multipliers Associated with Inequality
		  Constraints in an Augmented Lagrangian Multiplier Method},
  journal	= jota,
  year		= 2003,
  volume	= 119,
  number	= 2,
  pages		= {215--233}
}

###Article{	  avelinovicente:2003,
  author	= {C. P. Avelino and L. N. Vicente},
  title		= {Updating the Multipliers Associated with Inequality
		  Constraints in an Augmented Lagrangian Multiplier Method},
  journal	= jota,
  year		= 2003,
  volume	= 119,
  number	= 2,
  pages		= {215--233}
}

@Article{	  avrontoledo:2011,
  author	= {Avron, Haim and Toledo, Sivan},
  title		= {Randomized algorithms for estimating the trace of an
		  implicit symmetric positive semi-definite matrix},
  journal	= {J. ACM},
  issue_date	= {April 2011},
  volume	= 58,
  issue		= 2,
  month		= {April},
  year		= 2011,
  issn		= {0004-5411},
  pages		= {8:1--8:34},
  articleno	= 8,
  numpages	= 34,
  url		= {http://doi.acm.org/10.1145/1944345.1944349},
  doi		= {http://doi.acm.org/10.1145/1944345.1944349},
  acmid		= 1944349,
  publisher	= {ACM},
  address	= {New York, NY, USA},
  keywords	= {Trace estimation, implicit linear operators}
}

###Article{	  avrontoledo:2011,
  author	= {Avron, Haim and Toledo, Sivan},
  title		= {Randomized algorithms for estimating the trace of an
		  implicit symmetric positive semi-definite matrix},
  journal	= {J. ACM},
  issue_date	= {April 2011},
  volume	= 58,
  issue		= 2,
  month		= {April},
  year		= 2011,
  pages		= {8:1--8:34},
  articleno	= 8,
  numpages	= 34,
  url		= {http://doi.acm.org/10.1145/1944345.1944349},
  doi		= {http://doi.acm.org/10.1145/1944345.1944349},
  acmid		= 1944349,
  publisher	= {ACM},
  address	= {New York, NY, USA},
  keywords	= {Trace estimation, implicit linear operators}
}

###Article{	  avrontoledo:2011,
  author	= {Avron, Haim and Toledo, Sivan},
  title		= {Randomized algorithms for estimating the trace of an
		  implicit symmetric positive semi-definite matrix},
  journal	= {J. ACM},
  issue_date	= {April 2011},
  volume	= 58,
  issue		= 2,
  month		= {April},
  year		= 2011,
  pages		= {8:1--8:34},
  articleno	= 8,
  numpages	= 34,
  url		= {http://doi.acm.org/10.1145/1944345.1944349},
  doi		= {http://doi.acm.org/10.1145/1944345.1944349},
  acmid		= 1944349,
  publisher	= {ACM},
  address	= {New York, NY, USA},
  keywords	= {Trace estimation, implicit linear operators}
}

@Article{	  azuma1967weighted,
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  date-modified	= {2012-10-30 21:08:27 +0000},
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  number	= 3,
  pages		= {357--367},
  title		= {Weighted sums of certain dependent random variables},
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  year		= 1967
}

@Article{	  bach2011learning,
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		  perspective},
  author	= {Bach, Francis},
  journal	= {arXiv preprint arXiv:1111.6453},
  year		= 2011
}

###Article{	  bach2011learning,
  title		= {Learning with submodular functions: A convex optimization
		  perspective},
  author	= {Bach, Francis},
  journal	= {arXiv preprint arXiv:1111.6453},
  year		= 2011
}

@Article{	  bach2012,
  ajournal	= "Statist. Sci.",
  author	= "Bach, Francis and Jenatton, Rodolphe and Mairal, Julien
		  and Obozinski, Guillaume",
  doi		= "10.1214/12-STS394",
  journal	= "Statistical Science",
  month		= 11,
  number	= 4,
  pages		= "450--468",
  publisher	= "The Institute of Mathematical Statistics",
  title		= "Structured Sparsity through Convex Optimization",
  volume	= 27,
  year		= 2012
}

###Article{	  bach2012,
  ajournal	= "Statist. Sci.",
  author	= "Bach, Francis and Jenatton, Rodolphe and Mairal, Julien
		  and Obozinski, Guillaume",
  doi		= "10.1214/12-STS394",
  journal	= "Statistical Science",
  month		= 11,
  number	= 4,
  pages		= "450--468",
  publisher	= "The Institute of Mathematical Statistics",
  title		= "Structured Sparsity through Convex Optimization",
  volume	= 27,
  year		= 2012
}

@Article{	  bach2012optimization,
  title		= {Optimization with sparsity-inducing penalties},
  author	= {Bach, Francis and Jenatton, Rodolphe and Mairal, Julien
		  and Obozinski, Guillaume},
  journal	= {Foundations and Trends{\textregistered} in Machine
		  Learning},
  volume	= 4,
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  pages		= {1--106},
  year		= 2012,
  publisher	= {Now Publishers Inc.}
}

###Article{	  bach2012optimization,
  title		= {Optimization with sparsity-inducing penalties},
  author	= {Bach, Francis and Jenatton, Rodolphe and Mairal, Julien
		  and Obozinski, Guillaume},
  journal	= {Foundations and Trends{\textregistered} in Machine
		  Learning},
  volume	= 4,
  number	= 1,
  pages		= {1--106},
  year		= 2012,
  publisher	= {Now Publishers Inc.}
}

@InCollection{	  bachthibjord:2005,
  address	= {San Mateo, CA},
  author	= {F. R. Bach and R. Thibaux and M. I. Jordan},
  booktitle	= {Advances in Neural Information Processing Systems (NIPS)
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  editor	= {L. Saul and Y. Weiss and L. Bottou},
  publisher	= {Morgan Kaufmann},
  title		= {Computing regularization paths for learning multiple
		  kernels},
  year		= 2005
}

###InCollection{  bachthibjord:2005,
  address	= {San Mateo, CA},
  author	= {F. R. Bach and R. Thibaux and M. I. Jordan},
  booktitle	= {Advances in Neural Inform. Processing Systems (NIPS) 17},
  editor	= {L. Saul and Y. Weiss and L. Bottou},
  publisher	= {Morgan Kaufmann},
  title		= {Computing regularization paths for learning multiple
		  kernels},
  year		= 2005
}

###InCollection{  bachthibjord:2005,
  address	= {San Mateo, CA},
  author	= {F. R. Bach and R. Thibaux and M. I. Jordan},
  booktitle	= {Advances in Neural Information Processing Systems (NIPS)
		  17},
  editor	= {L. Saul and Y. Weiss and L. Bottou},
  publisher	= {Morgan Kaufmann},
  title		= {Computing regularization paths for learning multiple
		  kernels},
  year		= 2005
}

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		  geometric programming},
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		  Jean-Philippe and Du Merle, Olivier},
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@Unpublished{	  balabuscgropkausknep:2001,
  author	= {Satish Balay and Kris Buschelman and William D. Gropp and
		  Dinesh Kaushik and Matt Knepley and Lois Curfman McInnes
		  and Barry F. Smith and Hong Zhang},
  note		= {http://www.mcs.anl.gov/petsc},
  title		= {{PETSc} home page},
  year		= 2001
}

###Unpublished{	  balabuscgropkausknep:2001,
  author	= {Satish Balay and Kris Buschelman and William D. Gropp and
		  Dinesh Kaushik and Matt Knepley and Lois Curfman McInnes
		  and Barry F. Smith and Hong Zhang},
  note		= {http://www.mcs.anl.gov/petsc},
  title		= {{PETSc} home page},
  year		= 2001
}

###Unpublished{	  balabuscgropkausknep:2001,
  author	= {Satish Balay and Kris Buschelman and William D. Gropp and
		  Dinesh Kaushik and Matt Knepley and Lois Curfman McInnes
		  and Barry F. Smith and Hong Zhang},
  note		= {http://www.mcs.anl.gov/petsc},
  title		= {{PETSc} home page},
  year		= 2001
}

@TechReport{	  balabuscgropkausknep:2002,
  author	= {Satish Balay and Kris Buschelman and William D. Gropp and
		  Dinesh Kaushik and Matt Knepley and Lois Curfman McInnes
		  and Barry F. Smith and Hong Zhang},
  institution	= {Argonne National Laboratory},
  address	= {Argonne, IL},
  number	= {ANL-95/11},
  title		= {{PETSc} Users Manual Revision 2.1.3},
  year		= 2002,
  type		= {Tech. rep.}
}

###TechReport{	  balabuscgropkausknep:2002,
  author	= {Satish Balay and Kris Buschelman and William D. Gropp and
		  Dinesh Kaushik and Matt Knepley and Lois Curfman McInnes
		  and Barry F. Smith and Hong Zhang},
  institution	= {Argonne National Laboratory},
  address	= {Argonne, IL},
  number	= {ANL-95/11},
  title		= {{PETSc} Users Manual Revision 2.1.3},
  year		= 2002,
  type		= {Tech. rep.}
}

###TechReport{	  balabuscgropkausknep:2002,
  author	= {Satish Balay and Kris Buschelman and William D. Gropp and
		  Dinesh Kaushik and Matt Knepley and Lois Curfman McInnes
		  and Barry F. Smith and Hong Zhang},
  institution	= {Argonne National Laboratory},
  address	= {Argonne, IL},
  number	= {ANL-95/11},
  title		= {{PETSc} Users Manual Revision 2.1.3},
  year		= 2002,
  type		= {Tech. rep.}
}

@InProceedings{	  balagropmcinsmit:1997,
  author	= {Satish Balay and William D. Gropp and Lois Curfman McInnes
		  and Barry F. Smith},
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  editor	= {E. Arge and A. M. Bruaset and H. P. Langtangen},
  pages		= {163--202},
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  address	= {Boston},
  title		= {Efficient Management of Parallelism in Object-Oriented
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  year		= 1997
}

###InProceedings{ balagropmcinsmit:1997,
  author	= {Satish Balay and William D. Gropp and Lois Curfman McInnes
		  and Barry F. Smith},
  booktitle	= {Modern Software Tools for Scientific Computing},
  editor	= {E. Arge and A. M. Bruaset and H. P. Langtangen},
  pages		= {163--202},
  publisher	= {Birkh\:{a}user Press},
  address	= {Boston},
  title		= {Efficient Management of Parallelism in Object-Oriented
		  Numerical Software Libraries},
  year		= 1997
}

###InProceedings{ balagropmcinsmit:1997,
  author	= {Satish Balay and William D. Gropp and Lois Curfman McInnes
		  and Barry F. Smith},
  booktitle	= {Modern Software Tools for Scientific Computing},
  editor	= {E. Arge and A. M. Bruaset and H. P. Langtangen},
  pages		= {163--202},
  publisher	= {Birkh\:{a}user Press},
  address	= {Boston},
  title		= {Efficient Management of Parallelism in Object-Oriented
		  Numerical Software Libraries},
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@Article{	  barzborw:1988,
  author	= {J. Barzilai and J. M. Borwein},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= imanumerana,
  pages		= {141-148},
  title		= {Two-point step size gradient methods},
  volume	= 8,
  year		= 1988
}

###Article{	  barzborw:1988,
  author	= {J. Barzilai and J. M. Borwein},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= imanumerana,
  pages		= {141-148},
  title		= {Two-point step size gradient methods},
  volume	= 8,
  year		= 1988
}

###Article{	  barzborw:1988,
  author	= {J. Barzilai and J. M. Borwein},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= imanumerana,
  pages		= {141-148},
  title		= {Two-point step size gradient methods},
  volume	= 8,
  year		= 1988
}

@InProceedings{	  bay05,
  title		= "Semidefinite programming algorithms for sensor network
		  localization using angle of arrival information",
  author	= "Pratik Biswas and Hamid Aghajan and Yinyu Ye",
  booktitle	= "39th Annual Asiloinar Conference on Signals, Systems, and
		  Computers",
  address	= "Pacific Grove, CA",
  year		= 2005
}

###InProceedings{ bay05,
  title		= "Semidefinite programming algorithms for sensor network
		  localization using angle of arrival information",
  author	= "Pratik Biswas and Hamid Aghajan and Yinyu Ye",
  booktitle	= "39th Annual Asiloinar Conference on Signals, Systems, and
		  Computers",
  address	= "Pacific Grove, CA",
  year		= 2005
}

###InProceedings{ bay05,
  title		= "Semidefinite programming algorithms for sensor network
		  localization using angle of arrival information",
  author	= "Pratik Biswas and Hamid Aghajan and Yinyu Ye",
  booktitle	= "39th Annual Asiloinar Conference on Signals, Systems, and
		  Computers",
  address	= "Pacific Grove, CA",
  year		= 2005
}

@Article{	  bbc03,
  author	= "Heinz H. Bauschke and Jonathan M. Borwein and Patrick L.
		  Combettes",
  title		= "{Bregman} Monotone Optimization Algorithms",
  journal	= siamcontrol,
  year		= 2003,
  volume	= 42,
  number	= 2,
  page		= {596--636}
}

###Article{	  bbc03,
  author	= "Heinz H. Bauschke and Jonathan M. Borwein and Patrick L.
		  Combettes",
  title		= "{Bregman} Monotone Optimization Algorithms",
  journal	= siamcontrol,
  year		= 2003,
  volume	= 42,
  number	= 2,
  page		= {596--636}
}

###Article{	  bbc03,
  author	= "Heinz H. Bauschke and Jonathan M. Borwein and Patrick L.
		  Combettes",
  title		= "{Bregman} Monotone Optimization Algorithms",
  journal	= siamcontrol,
  year		= 2003,
  volume	= 42,
  number	= 2,
  page		= {596--636}
}

@TechReport{	  bbc09,
  title		= {NESTA: A Fast and Accurate First-order Method for Sparse
		  Recovery},
  author	= {S. Becker and J. Bobin and E. Cand\'es},
  institution	= {California Institute of Technology},
  type		= {Tech. Rep.},
  month		= {April},
  year		= 2009
}

###TechReport{	  bbc09,
  title		= {NESTA: A Fast and Accurate First-order Method for Sparse
		  Recovery},
  author	= {S. Becker and J. Bobin and E. Cand\'es},
  institution	= {California Institute of Technology},
  type		= {Tech. Rep.},
  month		= {April},
  year		= 2009
}

###TechReport{	  bbc09,
  title		= {NESTA: A Fast and Accurate First-order Method for Sparse
		  Recovery},
  author	= {S. Becker and J. Bobin and E. Cand\'es},
  institution	= {California Institute of Technology},
  type		= {Tech. Rep.},
  month		= {April},
  year		= 2009
}

@Article{	  bddw08,
  author	= {Richard Baraniuk and Mark Davenport and Ronald DeVore and
		  Michael Wakin },
  title		= {A Simple Proof of the Restricted Isometry Property for
		  Random Matrices},
  journal	= {Constructive Approximation},
  year		= 2008,
  volume	= 28,
  number	= 3,
  pages		= {253--263},
  month		= {December}
}

###Article{	  bddw08,
  author	= {Richard Baraniuk and Mark Davenport and Ronald DeVore and
		  Michael Wakin },
  title		= {A Simple Proof of the Restricted Isometry Property for
		  Random Matrices},
  journal	= {Constructive Approximation},
  year		= 2008,
  volume	= 28,
  number	= 3,
  pages		= {253--263},
  month		= {December}
}

###Article{	  bddw08,
  author	= {Richard Baraniuk and Mark Davenport and Ronald DeVore and
		  Michael Wakin },
  title		= {A Simple Proof of the Restricted Isometry Property for
		  Random Matrices},
  journal	= {Constructive Approximation},
  year		= 2008,
  volume	= 28,
  number	= 3,
  pages		= {253--263},
  month		= {December}
}

@InBook{	  beal:1967,
  address	= {Amsterdam},
  author	= {E. M. L. Beale},
  chapter	= 7,
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. Abadie},
  publisher	= northholland,
  title		= {Nonlinear Programming},
  year		= 1967
}

###InBook{	  beal:1967,
  address	= {Amsterdam},
  author	= {E. M. L. Beale},
  chapter	= 7,
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. Abadie},
  publisher	= northholland,
  title		= {Nonlinear Programming},
  year		= 1967
}

###InBook{	  beal:1967,
  address	= {Amsterdam},
  author	= {E. M. L. Beale},
  chapter	= 7,
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. Abadie},
  publisher	= northholland,
  title		= {Nonlinear Programming},
  year		= 1967
}

@Unpublished{	  beaz:2001,
  author	= {David M. Beazley},
  date-modified	= {2007-07-18 14:59:33 -0700},
  note		= {{\tt http://systems.cs.uchicago.edu/ply/}},
  title		= {{PLY} {(Python Lex-Yacc)}},
  year		= 2001
}

###Unpublished{	  beaz:2001,
  author	= {David M. Beazley},
  date-modified	= {2007-07-18 14:59:33 -0700},
  note		= {{\tt http://systems.cs.uchicago.edu/ply/}},
  title		= {{PLY} {(Python Lex-Yacc)}},
  year		= 2001
}

###Unpublished{	  beaz:2001,
  author	= {David M. Beazley},
  date-modified	= {2007-07-18 14:59:33 -0700},
  note		= {{\tt http://systems.cs.uchicago.edu/ply/}},
  title		= {{PLY} {(Python Lex-Yacc)}},
  year		= 2001
}

@Article{	  beck2009fast,
  title		= {A fast iterative shrinkage-thresholding algorithm for
		  linear inverse problems},
  author	= {Beck, Amir and Teboulle, Marc},
  journal	= siamsiims,
  volume	= 2,
  number	= 1,
  pages		= {183--202},
  year		= 2009,
  publisher	= {SIAM}
}

###Article{	  beck2009fast,
  title		= {A fast iterative shrinkage-thresholding algorithm for
		  linear inverse problems},
  author	= {Beck, Amir and Teboulle, Marc},
  journal	= {SIAM Journal on Imaging Sciences},
  volume	= 2,
  number	= 1,
  pages		= {183--202},
  year		= 2009,
  publisher	= {SIAM}
}

@Article{	  beckbobcandgrant:2011,
  author	= {Becker, Stephen and Candès, Emmanuel and Grant, Michael},
  affiliation	= {Applied and Computational Mathematics, California
		  Institute of Technology, Pasadena, CA 91125, USA},
  title		= {Templates for convex cone problems with applications to
		  sparse signal recovery},
  journal	= mathprogc,
  issn		= {1867-2949},
  keyword	= {Mathematics and Statistics},
  pages		= {165-218},
  volume	= 3,
  issue		= 3,
  doi		= {10.1007/s12532-011-0029-5},
  abstract	= {This paper develops a general framework for solving a
		  variety of convex cone problems that frequently arise in
		  signal processing, machine learning, statistics, and other
		  fields. The approach works as follows: first, determine a
		  conic formulation of the problem; second, determine its
		  dual; third, apply smoothing; and fourth, solve using an
		  optimal first-order method. A merit of this approach is its
		  flexibility: for example, all compressed sensing problems
		  can be solved via this approach. These include models with
		  objective functionals such as the total-variation norm, ||
		  Wx || 1 where W is arbitrary, or a combination thereof. In
		  addition, the paper introduces a number of technical
		  contributions such as a novel continuation scheme and a
		  novel approach for controlling the step size, and applies
		  results showing that the smooth and unsmoothed problems are
		  sometimes formally equivalent. Combined with our framework,
		  these lead to novel, stable and computationally efficient
		  algorithms. For instance, our general implementation is
		  competitive with state-of-the-art methods for solving
		  intensively studied problems such as the LASSO. Further,
		  numerical experiments show that one can solve the Dantzig
		  selector problem, for which no efficient large-scale
		  solvers exist, in a few hundred iterations. Finally, the
		  paper is accompanied with a software release. This software
		  is not a single, monolithic solver; rather, it is a suite
		  of programs and routines designed to serve as building
		  blocks for constructing complete algorithms.},
  year		= 2011
}

###Article{	  beckbobcandgrant:2011,
  author	= {Becker, Stephen and Cand\`es, Emmanuel and Grant, Michael},
  affiliation	= {Applied and Computational Mathematics, California
		  Institute of Technology, Pasadena, CA 91125, USA},
  title		= {Templates for convex cone problems with applications to
		  sparse signal recovery},
  journal	= mathprogc,
  keyword	= {Mathematics and Statistics},
  pages		= {165-218},
  volume	= 3,
  issue		= 3,
  doi		= {10.1007/s12532-011-0029-5},
  abstract	= {This paper develops a general framework for solving a
		  variety of convex cone problems that frequently arise in
		  signal processing, machine learning, statistics, and other
		  fields. The approach works as follows: first, determine a
		  conic formulation of the problem; second, determine its
		  dual; third, apply smoothing; and fourth, solve using an
		  optimal first-order method. A merit of this approach is its
		  flexibility: for example, all compressed sensing problems
		  can be solved via this approach. These include models with
		  objective functionals such as the total-variation norm, ||
		  Wx || 1 where W is arbitrary, or a combination thereof. In
		  addition, the paper introduces a number of technical
		  contributions such as a novel continuation scheme and a
		  novel approach for controlling the step size, and applies
		  results showing that the smooth and unsmoothed problems are
		  sometimes formally equivalent. Combined with our framework,
		  these lead to novel, stable and computationally efficient
		  algorithms. For instance, our general implementation is
		  competitive with state-of-the-art methods for solving
		  intensively studied problems such as the LASSO. Further,
		  numerical experiments show that one can solve the Dantzig
		  selector problem, for which no efficient large-scale
		  solvers exist, in a few hundred iterations. Finally, the
		  paper is accompanied with a software release. This software
		  is not a single, monolithic solver; rather, it is a suite
		  of programs and routines designed to serve as building
		  blocks for constructing complete algorithms.},
  year		= 2011
}

###Article{	  beckbobcandgrant:2011,
  author	= {Becker, Stephen and Cand\`es, Emmanuel and Grant, Michael},
  affiliation	= {Applied and Computational Mathematics, California
		  Institute of Technology, Pasadena, CA 91125, USA},
  title		= {Templates for convex cone problems with applications to
		  sparse signal recovery},
  journal	= mathprogc,
  keyword	= {Mathematics and Statistics},
  pages		= {165-218},
  volume	= 3,
  issue		= 3,
  doi		= {10.1007/s12532-011-0029-5},
  abstract	= {This paper develops a general framework for solving a
		  variety of convex cone problems that frequently arise in
		  signal processing, machine learning, statistics, and other
		  fields. The approach works as follows: first, determine a
		  conic formulation of the problem; second, determine its
		  dual; third, apply smoothing; and fourth, solve using an
		  optimal first-order method. A merit of this approach is its
		  flexibility: for example, all compressed sensing problems
		  can be solved via this approach. These include models with
		  objective functionals such as the total-variation norm, ||
		  Wx || 1 where W is arbitrary, or a combination thereof. In
		  addition, the paper introduces a number of technical
		  contributions such as a novel continuation scheme and a
		  novel approach for controlling the step size, and applies
		  results showing that the smooth and unsmoothed problems are
		  sometimes formally equivalent. Combined with our framework,
		  these lead to novel, stable and computationally efficient
		  algorithms. For instance, our general implementation is
		  competitive with state-of-the-art methods for solving
		  intensively studied problems such as the LASSO. Further,
		  numerical experiments show that one can solve the Dantzig
		  selector problem, for which no efficient large-scale
		  solvers exist, in a few hundred iterations. Finally, the
		  paper is accompanied with a software release. This software
		  is not a single, monolithic solver; rather, it is a suite
		  of programs and routines designed to serve as building
		  blocks for constructing complete algorithms.},
  year		= 2011
}

@Article{	  beckbobicand:2011,
  title		= {NESTA: A Fast and Accurate First-Order Method for Sparse
		  Recovery},
  publisher	= {SIAM},
  year		= 2011,
  doi		= {DOI:10.1137/090756855},
  eissn		= 19364954,
  coden		= {SJISBI},
  volume	= 4,
  number	= 1,
  pages		= {1-39},
  author	= {Stephen Becker and Jérôme Bobin and Emmanuel J.
		  Candès},
  keywords	= {Nesterov's method; smooth approximations of nonsmooth
		  functions; $\ell_1$ minimization; duality in convex
		  optimization; continuation methods; compressed sensing;
		  total-variation minimization; 90C06; 90C25; 94A08; },
  url		= {http://dx.doi.org/doi/10.1137/090756855}
}

###Article{	  beckbobicand:2011,
  title		= {NESTA: A Fast and Accurate First-Order Method for Sparse
		  Recovery},
  publisher	= {SIAM},
  journal	= siamsiims,
  year		= 2011,
  doi		= {DOI:10.1137/090756855},
  coden		= {SJISBI},
  volume	= 4,
  number	= 1,
  pages		= {1-39},
  author	= {Stephen Becker and J\'er\^ome Bobin and Emmanuel J.
		  Cand\`es},
  keywords	= {Nesterov's method; smooth approximations of nonsmooth
		  functions; $\ell_1$ minimization; duality in convex
		  optimization; continuation methods; compressed sensing;
		  total-variation minimization; 90C06; 90C25; 94A08; }
}

###Article{	  beckbobicand:2011,
  title		= {NESTA: A Fast and Accurate First-Order Method for Sparse
		  Recovery},
  publisher	= {SIAM},
  journal	= siamsiims,
  year		= 2011,
  doi		= {DOI:10.1137/090756855},
  coden		= {SJISBI},
  volume	= 4,
  number	= 1,
  pages		= {1-39},
  author	= {Stephen Becker and J\'er\^ome Bobin and Emmanuel J.
		  Cand\`es},
  keywords	= {Nesterov's method; smooth approximations of nonsmooth
		  functions; $\ell_1$ minimization; duality in convex
		  optimization; continuation methods; compressed sensing;
		  total-variation minimization; 90C06; 90C25; 94A08; }
}

@Article{	  beckteboulle:2012,
  title		= {Smoothing and first order methods: A unified framework},
  author	= {Beck, Amir and Teboulle, Marc},
  journal	= siamopt,
  volume	= 22,
  number	= 2,
  pages		= {557--580},
  year		= 2012,
  publisher	= {SIAM}
}

###Article{	  beckteboulle:2012,
  title		= {Smoothing and first order methods: A unified framework},
  author	= {Beck, Amir and Teboulle, Marc},
  journal	= siamopt,
  volume	= 22,
  number	= 2,
  pages		= {557--580},
  year		= 2012,
  publisher	= {SIAM}
}

@Misc{		  beks14,
  abstract	= {The Julia programming language is gaining enormous
		  popularity. Julia was designed to be easy and fast. Most
		  importantly, Julia shatters deeply established notions
		  widely held in the applied community: 1. High-level,
		  dynamic code has to be slow by some sort of law of nature.
		  2. It is sensible to prototype in one language and then
		  recode in another language. 3. There are parts of a system
		  for the programmer, and other parts best left untouched as
		  they are built by the experts. Julia began with a deep
		  understanding of the needs of the scientific programmer and
		  the needs of the computer in mind. Bridging cultures that
		  have often been distant, Julia combines expertise from
		  computer science and computational science creating a new
		  approach to scientific computing. This note introduces the
		  programmer to the language and the underlying design
		  theory. It invites the reader to rethink the fundamental
		  foundations of numerical computing systems. In particular,
		  there is the fascinating dance between specialization and
		  abstraction. Specialization allows for custom treatment. We
		  can pick just the right algorithm for the right
		  circumstance and this can happen at runtime based on
		  argument types (code selection via multiple dispatch).
		  Abstraction recognizes what remains the same after
		  differences are stripped away and ignored as irrelevant.
		  The recognition of abstraction allows for code reuse
		  (generic programming). A simple idea that yields incredible
		  power. The Julia design facilitates this interplay in many
		  explicit and subtle ways for machine performance and, most
		  importantly, human convenience.},
  eprinttype	= {arXiv},
  author	= {Jeff Bezanson and Alan Edelman and Stefan Karpinski and
		  Viral B. Shah},
  eprint	= {1411.1607},
  month		= {November},
  eprintclass	= {cs.MS},
  title		= {{J}ulia: A Fresh Approach to Numerical Computing},
  year		= {2014}
}

###Misc{	  beks14,
  abstract	= {The Julia programming language is gaining enormous
		  popularity. Julia was designed to be easy and fast. Most
		  importantly, Julia shatters deeply established notions
		  widely held in the applied community: 1. High-level,
		  dynamic code has to be slow by some sort of law of nature.
		  2. It is sensible to prototype in one language and then
		  recode in another language. 3. There are parts of a system
		  for the programmer, and other parts best left untouched as
		  they are built by the experts. Julia began with a deep
		  understanding of the needs of the scientific programmer and
		  the needs of the computer in mind. Bridging cultures that
		  have often been distant, Julia combines expertise from
		  computer science and computational science creating a new
		  approach to scientific computing. This note introduces the
		  programmer to the language and the underlying design
		  theory. It invites the reader to rethink the fundamental
		  foundations of numerical computing systems. In particular,
		  there is the fascinating dance between specialization and
		  abstraction. Specialization allows for custom treatment. We
		  can pick just the right algorithm for the right
		  circumstance and this can happen at runtime based on
		  argument types (code selection via multiple dispatch).
		  Abstraction recognizes what remains the same after
		  differences are stripped away and ignored as irrelevant.
		  The recognition of abstraction allows for code reuse
		  (generic programming). A simple idea that yields incredible
		  power. The Julia design facilitates this interplay in many
		  explicit and subtle ways for machine performance and, most
		  importantly, human convenience.},
  eprinttype	= {arXiv},
  author	= {Jeff Bezanson and Alan Edelman and Stefan Karpinski and
		  Viral B. Shah},
  eprint	= {1411.1607},
  month		= {November},
  eprintclass	= {cs.MS},
  title		= {{J}ulia: A Fresh Approach to Numerical Computing},
  year		= {2014}
}

@Article{	  bellavia-2009,
  author	= {S. Bellavia and J. Gondzio and B. Morini},
  title		= {Regularization and Preconditioning of {KKT} Systems
		  Arising in Nonnegative Least-Squares Problems},
  journal	= {Numerical Linear Algebra and Applications},
  year		= 2009,
  doi		= {10.1002/nla.610},
  volume	= 16,
  number	= 1,
  pages		= {39--61}
}

###Article{	  bellavia-2009,
  author	= {S. Bellavia and J. Gondzio and B. Morini},
  title		= {Regularization and Preconditioning of {KKT} Systems
		  Arising in Nonnegative Least-Squares Problems},
  journal	= {Numerical Linear Algebra and Applications},
  year		= 2009,
  doi		= {10.1002/nla.610},
  volume	= 16,
  number	= 1,
  pages		= {39--61}
}

###Article{	  bellavia-2009,
  author	= {S. Bellavia and J. Gondzio and B. Morini},
  title		= {Regularization and Preconditioning of {KKT} Systems
		  Arising in Nonnegative Least-Squares Problems},
  journal	= {Numerical Linear Algebra and Applications},
  year		= 2009,
  doi		= {10.1002/nla.610},
  volume	= 16,
  number	= 1,
  pages		= {39--61}
}

@Book{		  ben-nemi:2001,
  address	= {Philadelphia},
  author	= {A. Ben-Tal and A. Nemirovski},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= siampub,
  series	= {MPS/SIAM Series on Optimization},
  title		= {Lectures on Modern Convex Optimization: {A}nalysis,
		  Algorithms, and Engineering Applications},
  volume	= 2,
  year		= 2001
}

###Book{	  ben-nemi:2001,
  address	= {Philadelphia},
  author	= {A. Ben-Tal and A. Nemirovski},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= siampub,
  series	= {MPS/SIAM Series on Optimization},
  title		= {Lectures on Modern Convex Optimization: {A}nalysis,
		  Algorithms, and Engineering Applications},
  volume	= 2,
  year		= 2001
}

###Book{	  ben-nemi:2001,
  address	= {Philadelphia},
  author	= {A. Ben-Tal and A. Nemirovski},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= siampub,
  series	= {MPS/SIAM Series on Optimization},
  title		= {Lectures on Modern Convex Optimization: {A}nalysis,
		  Algorithms, and Engineering Applications},
  volume	= 2,
  year		= 2001
}

@InProceedings{	  benscurfmoresar:2004,
  author	= {S. J. Benson and L. Curfman McInnes and J. J. Mor\'{e} and
		  J. Sarich},
  title		= {Scalable Algorithms in Optimization: Computational
		  Experiments},
  year		= 2004,
  booktitle	= {AIAA Multidisciplinary Analysis and Optimization},
  address	= {Albany, NY}
}

###InProceedings{ benscurfmoresar:2004,
  author	= {S. J. Benson and L. Curfman McInnes and J. J. Mor\'{e} and
		  J. Sarich},
  title		= {Scalable Algorithms in Optimization: Computational
		  Experiments},
  year		= 2004,
  booktitle	= {AIAA Multidisciplinary Analysis and Optimization},
  address	= {Albany, NY}
}

###InProceedings{ benscurfmoresar:2004,
  author	= {S. J. Benson and L. Curfman McInnes and J. J. Mor\'{e} and
		  J. Sarich},
  title		= {Scalable Algorithms in Optimization: Computational
		  Experiments},
  year		= 2004,
  booktitle	= {AIAA Multidisciplinary Analysis and Optimization},
  address	= {Albany, NY}
}

@TechReport{	  benssenshanvand:2003,
  author	= {Hande Y. Benson and Arun Sen and David F. Shanno and
		  Robert J. Vanderbei},
  institution	= {Operations Research and Financial Engineering, Princeton
		  University},
  number	= {ORFE-03-02},
  title		= {Interior-point algorithms, penalty methods and equilibrium
		  problems},
  year		= 2003
}

###TechReport{	  benssenshanvand:2003,
  author	= {Hande Y. Benson and Arun Sen and David F. Shanno and
		  Robert J. Vanderbei},
  institution	= {Operations Research and Financial Engineering, Princeton
		  University},
  number	= {ORFE-03-02},
  title		= {Interior-point algorithms, penalty methods and equilibrium
		  problems},
  year		= 2003
}

###TechReport{	  benssenshanvand:2003,
  author	= {Hande Y. Benson and Arun Sen and David F. Shanno and
		  Robert J. Vanderbei},
  institution	= {Operations Research and Financial Engineering, Princeton
		  University},
  number	= {ORFE-03-02},
  title		= {Interior-point algorithms, penalty methods and equilibrium
		  problems},
  year		= 2003
}

@Misc{		  bensshan:2007,
  author	= {H. Y. Benson and D. Shanno},
  howpublished	= {To appear in {\it Comp. Optim. Appl.}},
  title		= {Interior-point methods for nonconvex nonlinear
		  programming: regularization and warmstarts},
  year		= 2007
}

###Misc{	  bensshan:2007,
  author	= {H. Y. Benson and D. Shanno},
  howpublished	= {To appear in {\it Comp. Optim. Appl.}},
  title		= {Interior-point methods for nonconvex nonlinear
		  programming: regularization and warmstarts},
  year		= 2007
}

###Misc{	  bensshan:2007,
  author	= {H. Y. Benson and D. Shanno},
  howpublished	= {To appear in {\it Comp. Optim. Appl.}},
  title		= {Interior-point methods for nonconvex nonlinear
		  programming: regularization and warmstarts},
  year		= 2007
}

@TechReport{	  bensshanvand:2002,
  author	= {Hande Y. Benson and David F. Shanno and Robert J.
		  Vanderbei},
  institution	= {Operations Research and Financial Engineering, Princeton
		  University},
  number	= {ORFE-02-02},
  title		= {Interior-point methods for nonconvex nonlinear
		  programming: complementarity constraints},
  year		= 2002
}

###TechReport{	  bensshanvand:2002,
  author	= {Hande Y. Benson and David F. Shanno and Robert J.
		  Vanderbei},
  institution	= {Operations Research and Financial Engineering, Princeton
		  University},
  number	= {ORFE-02-02},
  title		= {Interior-point methods for nonconvex nonlinear
		  programming: complementarity constraints},
  year		= 2002
}

###TechReport{	  bensshanvand:2002,
  author	= {Hande Y. Benson and David F. Shanno and Robert J.
		  Vanderbei},
  institution	= {Operations Research and Financial Engineering, Princeton
		  University},
  number	= {ORFE-02-02},
  title		= {Interior-point methods for nonconvex nonlinear
		  programming: complementarity constraints},
  year		= 2002
}

@Article{	  benzgolu:2004,
  abstract	= {In this paper we consider the solution of linear systems
		  of saddle point type by preconditioned Krylov subspace
		  methods. A preconditioning strategy based on the symmetric/
		  skew-symmetric splitting of the coefficient matrix is
		  proposed, and some useful properties of the preconditioned
		  matrix are established. The potential of this approach is
		  illustrated by numerical experiments with matrices from
		  various application areas.},
  author	= {M. Benzi and G. H. Golub},
  journal	= siammatrix,
  number	= 1,
  pages		= {20-41},
  title		= {A preconditioner for generalized saddle point problems},
  volume	= 26,
  year		= 2004
}

###Article{	  benzgolu:2004,
  abstract	= {In this paper we consider the solution of linear systems
		  of saddle point type by preconditioned Krylov subspace
		  methods. A preconditioning strategy based on the symmetric/
		  skew-symmetric splitting of the coefficient matrix is
		  proposed, and some useful properties of the preconditioned
		  matrix are established. The potential of this approach is
		  illustrated by numerical experiments with matrices from
		  various application areas.},
  author	= {M. Benzi and G. H. Golub},
  journal	= siammatrix,
  number	= 1,
  pages		= {20-41},
  title		= {A preconditioner for generalized saddle point problems},
  volume	= 26,
  year		= 2004
}

###Article{	  benzgolu:2004,
  abstract	= {In this paper we consider the solution of linear systems
		  of saddle point type by preconditioned Krylov subspace
		  methods. A preconditioning strategy based on the symmetric/
		  skew-symmetric splitting of the coefficient matrix is
		  proposed, and some useful properties of the preconditioned
		  matrix are established. The potential of this approach is
		  illustrated by numerical experiments with matrices from
		  various application areas.},
  author	= {M. Benzi and G. H. Golub},
  journal	= siammatrix,
  number	= 1,
  pages		= {20-41},
  title		= {A preconditioner for generalized saddle point problems},
  volume	= 26,
  year		= 2004
}

@Article{	  benzgolulies:2005,
  author	= {M. Benzi and G. Golub and J. Liesen},
  journal	= actanumerica,
  month		= {April},
  pages		= {1--137},
  title		= {Numerical solution of saddle point problems},
  volume	= 14,
  year		= 2005
}

###Article{	  benzgolulies:2005,
  author	= {M. Benzi and G. Golub and J. Liesen},
  journal	= actanumerica,
  month		= {April},
  pages		= {1--137},
  title		= {Numerical solution of saddle point problems},
  volume	= 14,
  year		= 2005
}

###Article{	  benzgolulies:2005,
  author	= {M. Benzi and G. Golub and J. Liesen},
  journal	= actanumerica,
  month		= {April},
  pages		= {1--137},
  title		= {Numerical solution of saddle point problems},
  volume	= 14,
  year		= 2005
}

@InCollection{	  benzi2008some,
  title		= {Some preconditioning techniques for saddle point
		  problems},
  author	= {Benzi, Michele and Wathen, Andrew J},
  booktitle	= {Model order reduction: theory, research aspects and
		  applications},
  pages		= {195--211},
  year		= 2008,
  publisher	= {Springer}
}

###InCollection{  benzi2008some,
  title		= {Some preconditioning techniques for saddle point
		  problems},
  author	= {Benzi, Michele and Wathen, Andrew J},
  booktitle	= {Model order reduction: theory, research aspects and
		  applications},
  pages		= {195--211},
  year		= 2008,
  publisher	= {Springer}
}

@PhDThesis{	  berg:2009,
  author	= {E. van den Berg},
  title		= {Convex optimization for generalized sparse recovery},
  school	= {University of British Columbia},
  year		= 2009,
  month		= {December}
}

@Article{	  berggondzill:2004,
  author	= {Luca Bergamaschi and Jacek Gondzio and Giovanni Zilli},
  journal	= compapplopt,
  month		= {July},
  number	= 2,
  pages		= {149-171},
  title		= {Preconditioning Indefinite Systems in Interior Point
		  Methods for Optimization},
  volume	= 28,
  year		= 2004
}

###Article{	  berggondzill:2004,
  author	= {Luca Bergamaschi and Jacek Gondzio and Giovanni Zilli},
  journal	= compapplopt,
  month		= {July},
  number	= 2,
  pages		= {149-171},
  title		= {Preconditioning Indefinite Systems in Interior Point
		  Methods for Optimization},
  volume	= 28,
  year		= 2004
}

###Article{	  berggondzill:2004,
  author	= {Luca Bergamaschi and Jacek Gondzio and Giovanni Zilli},
  journal	= compapplopt,
  month		= {July},
  number	= 2,
  pages		= {149-171},
  title		= {Preconditioning Indefinite Systems in Interior Point
		  Methods for Optimization},
  volume	= 28,
  year		= 2004
}

@Book{		  bermshak:2003,
  title		= {Completely positive matrices},
  author	= {A. Bermanand and N. Shaked-Monderer},
  year		= 2003,
  publisher	= {World Scientific}
}

###Book{	  bermshak:2003,
  title		= {Completely positive matrices},
  author	= {A. Bermanand and N. Shaked-Monderer},
  year		= 2003,
  publisher	= {World Scientific}
}

###Book{	  bermshak:2003,
  title		= {Completely positive matrices},
  author	= {A. Bermanand and N. Shaked-Monderer},
  year		= 2003,
  publisher	= {World Scientific}
}

@Article{	  bert:1975,
  author	= {D. P. Bertsekas},
  journal	= mathprog,
  pages		= {87--99},
  title		= {Necessary and sufficient conditions for a penalty method
		  to be exact},
  volume	= 9,
  year		= 1975
}

###Article{	  bert:1975,
  author	= {D. P. Bertsekas},
  journal	= mathprog,
  pages		= {87--99},
  title		= {Necessary and sufficient conditions for a penalty method
		  to be exact},
  volume	= 9,
  year		= 1975
}

###Article{	  bert:1975,
  author	= {D. P. Bertsekas},
  journal	= mathprog,
  pages		= {87--99},
  title		= {Necessary and sufficient conditions for a penalty method
		  to be exact},
  volume	= 9,
  year		= 1975
}

@Book{		  bert:1982,
  address	= {New York},
  author	= {D. P. Bertsekas},
  publisher	= academic,
  title		= {Constrained Optimization and {L}agrange Multiplier
		  Methods},
  year		= 1982
}

###Book{	  bert:1982,
  address	= {New York},
  author	= {D. P. Bertsekas},
  publisher	= academic,
  title		= {Constrained Optimization and {L}agrange Multiplier
		  Methods},
  year		= 1982
}

###Book{	  bert:1982,
  address	= {New York},
  author	= {D. P. Bertsekas},
  publisher	= academic,
  title		= {Constrained Optimization and {L}agrange Multiplier
		  Methods},
  year		= 1982
}

@Book{		  bert:1999,
  address	= {Belmont, MA},
  author	= {D. P. Bertsekas},
  edition	= {Second},
  publisher	= athena,
  title		= {Nonlinear Programming},
  year		= 1999
}

###Book{	  bert:1999,
  address	= {Belmont, MA},
  author	= {D. P. Bertsekas},
  edition	= {Second},
  publisher	= athena,
  title		= {Nonlinear Programming},
  year		= 1999
}

###Book{	  bert:1999,
  address	= {Belmont, MA},
  author	= {D. P. Bertsekas},
  edition	= {Second},
  publisher	= athena,
  title		= {Nonlinear Programming},
  year		= 1999
}

@Book{		  bert:2003,
  address	= {Belmont, MA},
  author	= {D. P. Bertsekas},
  publisher	= athena,
  title		= {Convex Analysis and Optimization},
  year		= 2003
}

###Book{	  bert:2003,
  address	= {Belmont, MA},
  author	= {D. P. Bertsekas},
  publisher	= athena,
  title		= {Convex Analysis and Optimization},
  year		= 2003
}

###Book{	  bert:2003,
  address	= {Belmont, MA},
  author	= {D. P. Bertsekas},
  publisher	= athena,
  title		= {Convex Analysis and Optimization},
  year		= 2003
}

@Book{		  bertnediozda:2003,
  author	= {D. P. Bertsekas and A. Nedic and A. E Ozdaglar},
  title		= {Convex analysis and optimization},
  publisher	= {Athena Scientific},
  year		= 2003
}

###Book{	  bertnediozda:2003,
  author	= {D. P. Bertsekas and A. Nedic and A. E Ozdaglar},
  title		= {Convex analysis and optimization},
  publisher	= {Athena Scientific},
  year		= 2003
}

###Book{	  bertnediozda:2003,
  author	= {D. P. Bertsekas and A. Nedic and A. E Ozdaglar},
  title		= {Convex analysis and optimization},
  publisher	= {Athena Scientific},
  year		= 2003
}

@InCollection{	  bertsekas-incremental:2010,
  title		= {{Incremental Gradient, Subgradient, and Proximal Methods
		  for Convex Optimization: A Survey}},
  author	= {Bertsekas, D.P.},
  editor	= {Suvrit Sra and Sebastian Nowozin and Stephen J. Wright},
  booktitle	= {Optimization for machine learning},
  year		= 2012,
  pages		= {85--115},
  chapter	= 4,
  publisher	= {MIT Press}
}

###InCollection{  bertsekas-incremental:2010,
  title		= {{Incremental Gradient, Subgradient, and Proximal Methods
		  for Convex Optimization: A Survey}},
  author	= {Bertsekas, D.P.},
  editor	= {Suvrit Sra and Sebastian Nowozin and Stephen J. Wright},
  booktitle	= {Optimization for machine learning},
  year		= 2012,
  pages		= {85--115},
  chapter	= 4,
  publisher	= {MIT Press}
}

###InCollection{  bertsekas-incremental:2010,
  title		= {{Incremental Gradient, Subgradient, and Proximal Methods
		  for Convex Optimization: A Survey}},
  author	= {Bertsekas, D.P.},
  editor	= {Suvrit Sra and Sebastian Nowozin and Stephen J. Wright},
  booktitle	= {Optimization for machine learning},
  year		= 2012,
  pages		= {85--115},
  chapter	= 4,
  publisher	= {MIT Press}
}

@Book{		  bertsekas1996neuro,
  title		= {{Neuro-dynamic programming}},
  author	= {Bertsekas, D.P. and Tsitsiklis, J.N.},
  publisher	= {Athena Scientific},
  year		= 1996
}

###Book{	  bertsekas1996neuro,
  title		= {{Neuro-dynamic programming}},
  author	= {Bertsekas, D.P. and Tsitsiklis, J.N.},
  publisher	= {Athena Scientific},
  year		= 1996
}

###Book{	  bertsekas1996neuro,
  title		= {{Neuro-dynamic programming}},
  author	= {Bertsekas, D.P. and Tsitsiklis, J.N.},
  publisher	= {Athena Scientific},
  year		= 1996
}

@Article{	  bertsekas1997new,
  title		= {{A new class of incremental gradient methods for least
		  squares problems}},
  author	= {Bertsekas, D.P.},
  journal	= siamopt,
  volume	= 7,
  number	= 4,
  pages		= {913--926},
  year		= 1997,
  publisher	= {Citeseer}
}

###Article{	  bertsekas1997new,
  title		= {{A new class of incremental gradient methods for least
		  squares problems}},
  author	= {Bertsekas, D.P.},
  journal	= siamopt,
  volume	= 7,
  number	= 4,
  pages		= {913--926},
  year		= 1997,
  publisher	= {Citeseer}
}

###Article{	  bertsekas1997new,
  title		= {{A new class of incremental gradient methods for least
		  squares problems}},
  author	= {Bertsekas, D.P.},
  journal	= siamopt,
  volume	= 7,
  number	= 4,
  pages		= {913--926},
  year		= 1997,
  publisher	= {Citeseer}
}

@Book{		  bertsekas2009convex,
  title		= {Convex optimization theory},
  author	= {Bertsekas, Dimitri P},
  year		= 2009,
  publisher	= {Athena Scientific Belmont, MA}
}

###Book{	  bertsekas2009convex,
  title		= {Convex optimization theory},
  author	= {Bertsekas, Dimitri P},
  year		= 2009,
  publisher	= {Athena Scientific Belmont, MA}
}

@Article{	  bertsekas:2010,
  title		= {{Incremental Gradient, Subgradient, and Proximal Methods
		  for Convex Optimization: A Survey}},
  author	= {Bertsekas, D.P.},
  year		= 2010
}

@InCollection{	  bertsekas:2012,
  author	= {D. P. Bertsekas},
  title		= {Incremental gradient, subgradient, and proximal methods
		  for convex optimization: A survey},
  booktitle	= {Optimization for Machine Learning},
  publisher	= {MIT},
  year		= 2012,
  chapter	= 4,
  pages		= {85--119}
}

###InCollection{  bertsekas:2012,
  author	= {D. P. Bertsekas},
  title		= {Incremental gradient, subgradient, and proximal methods
		  for convex optimization: A survey},
  booktitle	= {Optimization for Machine Learning},
  publisher	= {MIT},
  year		= 2012,
  chapter	= 4,
  pages		= {85--119}
}

@Article{	  bertsimas2004solving,
  title		= {Solving convex programs by random walks},
  author	= {Bertsimas, Dimitris and Vempala, Santosh},
  journal	= {Journal of the ACM},
  volume	= {51},
  number	= {4},
  pages		= {540--556},
  year		= {2004},
  publisher	= {ACM}
}

@Book{		  berttsit:1997,
  author	= {Dimitris Bertsimas and John N. Tsitsiklis},
  title		= {Introduction to Linear Optimization},
  publisher	= {Athena Scientific},
  year		= 1997,
  address	= {Nashua, NH}
}

###Book{	  berttsit:1997,
  author	= {Dimitris Bertsimas and John N. Tsitsiklis},
  title		= {Introduction to Linear Optimization},
  publisher	= {Athena Scientific},
  year		= 1997,
  address	= {Nashua, NH}
}

###Book{	  berttsit:1997,
  author	= {Dimitris Bertsimas and John N. Tsitsiklis},
  title		= {Introduction to Linear Optimization},
  publisher	= {Athena Scientific},
  year		= 1997,
  address	= {Nashua, NH}
}

@Article{	  best1990,
  author	= "Best, Michael J. and Chakravarti, Nilotpal",
  title		= "Active set algorithms for isotonic regression; A unifying
		  framework",
  journal	= mathprog,
  year		= 1990,
  volume	= 47,
  number	= 1,
  pages		= "425--439",
  abstract	= "In this and subsequent papers we will show that several
		  algorithms for the isotonic regression problem may be
		  viewed as active set methods. The active set approach
		  provides a unifying framework for studying algorithms for
		  isotonic regression, simplifies the exposition of existing
		  algorithms and leads to several new efficient algorithms.
		  We also investigate the computational complexity of several
		  algorithms."
}

@Article{	  bestbraurittrobi:1981,
  author	= {M. J. Best and J. Br{\"a}uninger and K. Ritter and S. M.
		  Robinson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= computing,
  pages		= {141--155},
  title		= {A Globally and Quadratically Convergent Algorithm for
		  General Nonlinear Programming Problems},
  volume	= 26,
  year		= 1981
}

###Article{	  bestbraurittrobi:1981,
  author	= {M. J. Best and J. Br{\"a}uninger and K. Ritter and S. M.
		  Robinson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= computing,
  pages		= {141--155},
  title		= {A Globally and Quadratically Convergent Algorithm for
		  General Nonlinear Programming Problems},
  volume	= 26,
  year		= 1981
}

###Article{	  bestbraurittrobi:1981,
  author	= {M. J. Best and J. Br{\"a}uninger and K. Ritter and S. M.
		  Robinson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= computing,
  pages		= {141--155},
  title		= {A Globally and Quadratically Convergent Algorithm for
		  General Nonlinear Programming Problems},
  volume	= 26,
  year		= 1981
}

@Article{	  bet03,
  author	= "Amir Beck and Marc Teboulle",
  title		= "Mirror descent and nonlinear projected subgradient methods
		  for convex optimization",
  journal	= "Operations Research Letters",
  year		= 2003,
  volume	= 31,
  page		= {167--175}
}

###Article{	  bet03,
  author	= "Amir Beck and Marc Teboulle",
  title		= "Mirror descent and nonlinear projected subgradient methods
		  for convex optimization",
  journal	= "Operations Research Letters",
  year		= 2003,
  volume	= 31,
  page		= {167--175}
}

###Article{	  bet03,
  author	= "Amir Beck and Marc Teboulle",
  title		= "Mirror descent and nonlinear projected subgradient methods
		  for convex optimization",
  journal	= "Operations Research Letters",
  year		= 2003,
  volume	= 31,
  page		= {167--175}
}

@Article{	  bet06,
  author	= "Amir Beck and Marc Teboulle",
  title		= "A Linearly Convergent Dual-Based Gradient Projection
		  Algorithm for Quadratically Constrained Convex
		  Minimization",
  journal	= mathofor,
  year		= 2006,
  volume	= 31,
  number	= 2,
  page		= {398--417},
  doi		= {10.1287/moor.1060.019}
}

###Article{	  bet06,
  author	= "Amir Beck and Marc Teboulle",
  title		= "A Linearly Convergent Dual-Based Gradient Projection
		  Algorithm for Quadratically Constrained Convex
		  Minimization",
  journal	= mathofor,
  year		= 2006,
  volume	= 31,
  number	= 2,
  page		= {398--417},
  doi		= {10.1287/moor.1060.019}
}

###Article{	  bet06,
  author	= "Amir Beck and Marc Teboulle",
  title		= "A Linearly Convergent Dual-Based Gradient Projection
		  Algorithm for Quadratically Constrained Convex
		  Minimization",
  journal	= mathofor,
  year		= 2006,
  volume	= 31,
  number	= 2,
  page		= {398--417},
  doi		= {10.1287/moor.1060.019}
}

@TechReport{	  bet08,
  author	= "Amir Beck and Marc Teboulle",
  title		= "A Fast Iterative Shrinkage-Thresholding Algorithm for
		  Linear Inverse Problems",
  institution	= "Department of Industrial Engineering and Management,
		  Technion",
  address	= "Haifa",
  year		= 2008
}

###TechReport{	  bet08,
  author	= "Amir Beck and Marc Teboulle",
  title		= "A Fast Iterative Shrinkage-Thresholding Algorithm for
		  Linear Inverse Problems",
  institution	= "Department of Industrial Engineering and Management,
		  Technion",
  address	= "Haifa",
  year		= 2008
}

###TechReport{	  bet08,
  author	= "Amir Beck and Marc Teboulle",
  title		= "A Fast Iterative Shrinkage-Thresholding Algorithm for
		  Linear Inverse Problems",
  institution	= "Department of Industrial Engineering and Management,
		  Technion",
  address	= "Haifa",
  year		= 2008
}

@Article{	  bettsfrank:1994,
  title		= {A sparse nonlinear optimization algorithm},
  author	= {Betts, J.T. and Frank, P.D.},
  journal	= {Journal of Optimization Theory and Applications},
  volume	= 82,
  number	= 3,
  pages		= {519--541},
  year		= 1994,
  publisher	= {Springer}
}

###Article{	  bettsfrank:1994,
  title		= {A sparse nonlinear optimization algorithm},
  author	= {Betts, J.T. and Frank, P.D.},
  journal	= {Journal of Optimization Theory and Applications},
  volume	= 82,
  number	= 3,
  pages		= {519--541},
  year		= 1994,
  publisher	= {Springer}
}

###Article{	  bettsfrank:1994,
  title		= {A sparse nonlinear optimization algorithm},
  author	= {Betts, J.T. and Frank, P.D.},
  journal	= {Journal of Optimization Theory and Applications},
  volume	= 82,
  number	= 3,
  pages		= {519--541},
  year		= 1994,
  publisher	= {Springer}
}

@TechReport{	  bettsfrank:1997,
  author	= {J.T. Betts and W.P. Huffman},
  title		= {Sparse optimal control software {SOCS}},
  type		= {Mathematics and Engineering Analysis Technical Document},
  number	= {MEA-LR-085},
  institution	= {Boeing Information and Support Services, The Boeing
		  Company},
  address	= {PO Box 3707, Seattle, WA 98124-2207},
  month		= {July},
  year		= 1997
}

###TechReport{	  bettsfrank:1997,
  author	= {J.T. Betts and W.P. Huffman},
  title		= {Sparse optimal control software {SOCS}},
  type		= {Mathematics and Engineering Analysis Technical Document},
  number	= {MEA-LR-085},
  institution	= {Boeing Information and Support Services, The Boeing
		  Company},
  address	= {PO Box 3707, Seattle, WA 98124-2207},
  month		= {July},
  year		= 1997
}

###TechReport{	  bettsfrank:1997,
  author	= {J.T. Betts and W.P. Huffman},
  title		= {Sparse optimal control software {SOCS}},
  type		= {Mathematics and Engineering Analysis Technical Document},
  number	= {MEA-LR-085},
  institution	= {Boeing Information and Support Services, The Boeing
		  Company},
  address	= {PO Box 3707, Seattle, WA 98124-2207},
  month		= {July},
  year		= 1997
}

@Article{	  bgd08,
  author	= "Onureena Banerjee and Laurent El Ghaoui and Alexandre
		  {\noopsort{Asprement}}{D'Aspremont}",
  title		= "Model Selection Through Sparse Maximum Likelihood
		  Estimation for Multivariate Gaussian or Binary Data",
  journal	= "J. Mach. Learn. Res.",
  year		= 2008,
  volume	= 9,
  page		= {485--516}
}

###Article{	  bgd08,
  author	= "Onureena Banerjee and Laurent El Ghaoui and Alexandre
		  {\noopsort{Asprement}}{D'Aspremont}",
  title		= "Model Selection Through Sparse Maximum Likelihood
		  Estimation for Multivariate Gaussian or Binary Data",
  journal	= "J. Mach. Learn. Res.",
  year		= 2008,
  volume	= 9,
  page		= {485--516}
}

###Article{	  bgd08,
  author	= "Onureena Banerjee and Laurent El Ghaoui and Alexandre
		  {\noopsort{Asprement}}{D'Aspremont}",
  title		= "Model Selection Through Sparse Maximum Likelihood
		  Estimation for Multivariate Gaussian or Binary Data",
  journal	= "J. Mach. Learn. Res.",
  year		= 2008,
  volume	= 9,
  page		= {485--516}
}

@Article{	  bhg07,
  author	= "Doron Blatt and Alfred O. Hero and Hillel Gauchman",
  title		= "A Convergent Incremental Gradient Method with a Constant
		  Step Size",
  journal	= siamopt,
  year		= 2007,
  volume	= 18,
  page		= {29--51}
}

###Article{	  bhg07,
  author	= "Doron Blatt and Alfred O. Hero and Hillel Gauchman",
  title		= "A Convergent Incremental Gradient Method with a Constant
		  Step Size",
  journal	= siamopt,
  year		= 2007,
  volume	= 18,
  page		= {29--51}
}

###Article{	  bhg07,
  author	= "Doron Blatt and Alfred O. Hero and Hillel Gauchman",
  title		= "A Convergent Incremental Gradient Method with a Constant
		  Step Size",
  journal	= siamopt,
  year		= 2007,
  volume	= 18,
  page		= {29--51}
}

@InCollection{	  bigg:1972,
  address	= {London},
  author	= {M. C. Biggs},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {F. A. Lootsma},
  publisher	= academic,
  title		= {Constrained minimization using recursive equality
		  quadratic programming},
  year		= 1972
}

###InCollection{  bigg:1972,
  address	= {London},
  author	= {M. C. Biggs},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {F. A. Lootsma},
  publisher	= academic,
  title		= {Constrained minimization using recursive equality
		  quadratic programming},
  year		= 1972
}

###InCollection{  bigg:1972,
  address	= {London},
  author	= {M. C. Biggs},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {F. A. Lootsma},
  publisher	= academic,
  title		= {Constrained minimization using recursive equality
		  quadratic programming},
  year		= 1972
}

@Article{	  birgmartrayd:2000,
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {1196-1211},
  title		= {Nonmonotone spectral projected gradient methods on convex
		  sets},
  volume	= 10,
  year		= 2000
}

###Article{	  birgmartrayd:2000,
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {1196-1211},
  title		= {Nonmonotone spectral projected gradient methods on convex
		  sets},
  volume	= 10,
  year		= 2000
}

###Article{	  birgmartrayd:2000,
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {1196-1211},
  title		= {Nonmonotone spectral projected gradient methods on convex
		  sets},
  volume	= 10,
  year		= 2000
}

@Article{	  birgmartrayd:2001,
  abstract	= {Fortran 77 software implementing the SPG method is
		  introduced. SPG is a nonmonotone projected gradient
		  algorithm for solving large-scale convex-constrained
		  optimization problems. It combines the classical projected
		  gradient method with the spectral gradient choice of
		  steplength and a nonmonotone line-search strategy. The user
		  provides objective function and gradient values, and
		  projections onto the feasible set. Some recent numerical
		  tests are reported on very large location problems,
		  indicating that SPG is substantially more efficient than
		  existing general-purpose software on problems for which
		  projections can be computed efficiently.},
  address	= {New York, NY, USA},
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-added	= {2007-06-11 21:18:11 -0700},
  date-modified	= {2007-07-18 14:59:32 -0700},
  doi		= {http://doi.acm.org/10.1145/502800.502803},
  issn		= {0098-3500},
  journal	= acmmathsoft,
  local-url	= {file://localhost/Users/mpf/papers/BirgMartRayd01.pdf},
  number	= 3,
  pages		= {340--349},
  publisher	= {ACM Press},
  title		= {Algorithm 813: {SPG}---{S}oftware for Convex-Constrained
		  Optimization},
  volume	= 27,
  year		= 2001
}

###Article{	  birgmartrayd:2001,
  abstract	= {Fortran 77 software implementing the SPG method is
		  introduced. SPG is a nonmonotone projected gradient
		  algorithm for solving large-scale convex-constrained
		  optimization problems. It combines the classical projected
		  gradient method with the spectral gradient choice of
		  steplength and a nonmonotone line-search strategy. The user
		  provides objective function and gradient values, and
		  projections onto the feasible set. Some recent numerical
		  tests are reported on very large location problems,
		  indicating that SPG is substantially more efficient than
		  existing general-purpose software on problems for which
		  projections can be computed efficiently.},
  address	= {New York, NY, USA},
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-added	= {2007-06-11 21:18:11 -0700},
  date-modified	= {2007-07-18 14:59:32 -0700},
  doi		= {http://doi.acm.org/10.1145/502800.502803},
  journal	= acmmathsoft,
  local-url	= {file://localhost/Users/mpf/papers/BirgMartRayd01.pdf},
  number	= 3,
  pages		= {340--349},
  publisher	= {ACM Press},
  title		= {Algorithm 813: {SPG}---{S}oftware for Convex-Constrained
		  Optimization},
  volume	= 27,
  year		= 2001
}

###Article{	  birgmartrayd:2001,
  abstract	= {Fortran 77 software implementing the SPG method is
		  introduced. SPG is a nonmonotone projected gradient
		  algorithm for solving large-scale convex-constrained
		  optimization problems. It combines the classical projected
		  gradient method with the spectral gradient choice of
		  steplength and a nonmonotone line-search strategy. The user
		  provides objective function and gradient values, and
		  projections onto the feasible set. Some recent numerical
		  tests are reported on very large location problems,
		  indicating that SPG is substantially more efficient than
		  existing general-purpose software on problems for which
		  projections can be computed efficiently.},
  address	= {New York, NY, USA},
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-added	= {2007-06-11 21:18:11 -0700},
  date-modified	= {2007-07-18 14:59:32 -0700},
  doi		= {http://doi.acm.org/10.1145/502800.502803},
  journal	= acmmathsoft,
  local-url	= {file://localhost/Users/mpf/papers/BirgMartRayd01.pdf},
  number	= 3,
  pages		= {340--349},
  publisher	= {ACM Press},
  title		= {Algorithm 813: {SPG}---{S}oftware for Convex-Constrained
		  Optimization},
  volume	= 27,
  year		= 2001
}

@Article{	  birgmartrayd:2003,
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= imanumerana,
  local-url	= {file://localhost/Users/mpf/papers/BirgMartRad03.pdf},
  pages		= {1196-1211},
  title		= {Inexact spectral projected gradient methods on convex
		  sets},
  volume	= 23,
  year		= 2003
}

###Article{	  birgmartrayd:2003,
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= imanumerana,
  local-url	= {file://localhost/Users/mpf/papers/BirgMartRad03.pdf},
  pages		= {1196-1211},
  title		= {Inexact spectral projected gradient methods on convex
		  sets},
  volume	= 23,
  year		= 2003
}

###Article{	  birgmartrayd:2003,
  author	= {E. G. Birgin and J. M. Mart\'{i}nez and M. Raydan},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= imanumerana,
  local-url	= {file://localhost/Users/mpf/papers/BirgMartRad03.pdf},
  pages		= {1196-1211},
  title		= {Inexact spectral projected gradient methods on convex
		  sets},
  volume	= 23,
  year		= 2003
}

@Article{	  biroghat:2005,
  author	= {G. Biros and O. Ghattas},
  date-modified	= {2007-07-18 14:59:33 -0700},
  doi		= {10.1137/S106482750241565X},
  journal	= siamscicomp,
  keywords	= {sequential quadratic programming; nonlinear equations;
		  parallel algorithms; adjoint methods; PDE-constrained
		  optimization; optimal control;
		  Lagrange--Newton--Krylov--Schur methods; Navier--Stokes;
		  finite elements; preconditioners; indefinite systems},
  number	= 2,
  pages		= {687-713},
  publisher	= {SIAM},
  title		= {Parallel Lagrange--Newton--Krylov--Schur Methods for
		  PDE-Constrained Optimization. Part I: The Krylov--Schur
		  Solver},
  url		= {http://link.aip.org/link/?SCE/27/687/1},
  volume	= 27,
  year		= 2005
}

###Article{	  biroghat:2005,
  author	= {G. Biros and O. Ghattas},
  date-modified	= {2007-07-18 14:59:33 -0700},
  doi		= {10.1137/S106482750241565X},
  journal	= siamscicomp,
  keywords	= {sequential quadratic programming; nonlinear equations;
		  parallel algorithms; adjoint methods; PDE-constrained
		  optimization; optimal control;
		  Lagrange--Newton--Krylov--Schur methods; Navier--Stokes;
		  finite elements; preconditioners; indefinite systems},
  number	= 2,
  pages		= {687-713},
  publisher	= {SIAM},
  title		= {Parallel Lagrange--Newton--Krylov--Schur Methods for
		  PDE-Constrained Optimization. Part I: The Krylov--Schur
		  Solver},
  url		= {http://link.aip.org/link/?SCE/27/687/1},
  volume	= 27,
  year		= 2005
}

###Article{	  biroghat:2005,
  author	= {G. Biros and O. Ghattas},
  date-modified	= {2007-07-18 14:59:33 -0700},
  doi		= {10.1137/S106482750241565X},
  journal	= siamscicomp,
  keywords	= {sequential quadratic programming; nonlinear equations;
		  parallel algorithms; adjoint methods; PDE-constrained
		  optimization; optimal control;
		  Lagrange--Newton--Krylov--Schur methods; Navier--Stokes;
		  finite elements; preconditioners; indefinite systems},
  number	= 2,
  pages		= {687-713},
  publisher	= {SIAM},
  title		= {Parallel Lagrange--Newton--Krylov--Schur Methods for
		  PDE-Constrained Optimization. Part I: The Krylov--Schur
		  Solver},
  url		= {http://link.aip.org/link/?SCE/27/687/1},
  volume	= 27,
  year		= 2005
}

@TechReport{	  bisccarlhovlkhadmaue:1998,
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		  Peyvand Khademi and Andrew Mauer},
  date-modified	= {2007-07-18 14:59:31 -0700},
  institution	= {Mathematics and Computer Science Division},
  month		= {June},
  number	= 192,
  title		= {{ADIFOR} 2.0 {Users' Guide}},
  year		= 1998
}

###TechReport{	  bisccarlhovlkhadmaue:1998,
  address	= {Argonne, IL},
  author	= {Christian Bischof and Alan Carle and Paul Hovland and
		  Peyvand Khademi and Andrew Mauer},
  date-modified	= {2007-07-18 14:59:31 -0700},
  institution	= {Mathematics and Computer Science Division},
  month		= {June},
  number	= 192,
  title		= {{ADIFOR} 2.0 {Users' Guide}},
  year		= 1998
}

###TechReport{	  bisccarlhovlkhadmaue:1998,
  address	= {Argonne, IL},
  author	= {Christian Bischof and Alan Carle and Paul Hovland and
		  Peyvand Khademi and Andrew Mauer},
  date-modified	= {2007-07-18 14:59:31 -0700},
  institution	= {Mathematics and Computer Science Division},
  month		= {June},
  number	= 192,
  title		= {{ADIFOR} 2.0 {Users' Guide}},
  year		= 1998
}

@Article{	  biscrohmaue:1997,
  author	= {Christian Bischof and Lucas Roh and A. Mauer},
  date-modified	= {2007-07-18 14:59:31 -0700},
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  number	= 12,
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  title		= {{ADIC}: An Extensible Automatic Differentiation Tool for
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  volume	= 27,
  year		= 1997
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###Article{	  biscrohmaue:1997,
  author	= {Christian Bischof and Lucas Roh and A. Mauer},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= {Software---Practice and Experience},
  month		= {December},
  number	= 12,
  pages		= {1427--1456},
  title		= {{ADIC}: An Extensible Automatic Differentiation Tool for
		  {ANSI-C}},
  volume	= 27,
  year		= 1997
}

###Article{	  biscrohmaue:1997,
  author	= {Christian Bischof and Lucas Roh and A. Mauer},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= {Software---Practice and Experience},
  month		= {December},
  number	= 12,
  pages		= {1427--1456},
  title		= {{ADIC}: An Extensible Automatic Differentiation Tool for
		  {ANSI-C}},
  volume	= 27,
  year		= 1997
}

@InBook{	  biy03,
  author	= "P. Biswas and Y. Ye",
  chapter	= "A Distributed Method for Solving Semidefinite Programs
		  Arising from Ad Hoc Wireless Sensor Network Localization",
  title		= siammms,
  series	= "Nonconvex Optimization and Its Applications",
  publisher	= "Springer",
  year		= 2003,
  volume	= 82
}

###InBook{	  biy03,
  author	= "P. Biswas and Y. Ye",
  chapter	= "A Distributed Method for Solving Semidefinite Programs
		  Arising from Ad Hoc Wireless Sensor Network Localization",
  title		= siammms,
  series	= "Nonconvex Optimization and Its Applications",
  publisher	= "Springer",
  year		= 2003,
  volume	= 82
}

###InBook{	  biy03,
  author	= "P. Biswas and Y. Ye",
  chapter	= "A Distributed Method for Solving Semidefinite Programs
		  Arising from Ad Hoc Wireless Sensor Network Localization",
  title		= siammms,
  series	= "Nonconvex Optimization and Its Applications",
  publisher	= "Springer",
  year		= 2003,
  volume	= 82
}

@InProceedings{	  biy04,
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  year		= 2004,
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  page		= {46--54}
}

###InProceedings{ biy04,
  author	= "Biswas, P. and Ye, Y.",
  title		= "Semidefinite programming for ad hoc wireless sensor
		  network localization",
  year		= 2004,
  booktitle	= "The 3rd international symposium on information processing
		  in sensor networks",
  page		= {46--54}
}

###InProceedings{ biy04,
  author	= "Biswas, P. and Ye, Y.",
  title		= "Semidefinite programming for ad hoc wireless sensor
		  network localization",
  year		= 2004,
  booktitle	= "The 3rd international symposium on information processing
		  in sensor networks",
  page		= {46--54}
}

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###Book{	  bjorck:1996,
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}

###Book{	  bjorck:1996,
  title		= {{Numerical Methods for Least Squares Problems}},
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  publisher	= {Society for Industrial Mathematics}
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@Article{	  blatt2008convergent,
  title		= {{A convergent incremental gradient method with a constant
		  step size}},
  author	= {Blatt, D. and Hero, A.O. and Gauchman, H.},
  journal	= siamopt,
  volume	= 18,
  number	= 1,
  pages		= {29--51},
  year		= 2008,
  publisher	= {Citeseer}
}

###Article{	  blatt2008convergent,
  title		= {{A convergent incremental gradient method with a constant
		  step size}},
  author	= {Blatt, D. and Hero, A.O. and Gauchman, H.},
  journal	= siamopt,
  volume	= 18,
  number	= 1,
  pages		= {29--51},
  year		= 2008,
  publisher	= {Citeseer}
}

###Article{	  blatt2008convergent,
  title		= {{A convergent incremental gradient method with a constant
		  step size}},
  author	= {Blatt, D. and Hero, A.O. and Gauchman, H.},
  journal	= siamopt,
  volume	= 18,
  number	= 1,
  pages		= {29--51},
  year		= 2008,
  publisher	= {Citeseer}
}

@Article{	  bltyw06,
  author	= "P. Biswas and Tzu-Chen Liang and Kim-Chuan Toh and
		  Ta-Chung Wang and Yinyu Ye",
  title		= "Semidefinite Programming Approaches for Sensor Network
		  Localization With Noisy Distance Measurements",
  journal	= "Automation Science and Engineering, IEEE Transactions",
  year		= 2006,
  volume	= 3,
  page		= {360--371}
}

###Article{	  bltyw06,
  author	= "P. Biswas and Tzu-Chen Liang and Kim-Chuan Toh and
		  Ta-Chung Wang and Yinyu Ye",
  title		= "Semidefinite Programming Approaches for Sensor Network
		  Localization With Noisy Distance Measurements",
  journal	= "Automation Science and Engineering, IEEE Transactions",
  year		= 2006,
  volume	= 3,
  page		= {360--371}
}

###Article{	  bltyw06,
  author	= "P. Biswas and Tzu-Chen Liang and Kim-Chuan Toh and
		  Ta-Chung Wang and Yinyu Ye",
  title		= "Semidefinite Programming Approaches for Sensor Network
		  Localization With Noisy Distance Measurements",
  journal	= "Automation Science and Engineering, IEEE Transactions",
  year		= 2006,
  volume	= 3,
  page		= {360--371}
}

@Article{	  blwy06,
  author	= "Biswas, P. and Liang, T.-C. and Wang, T.-C. and Ye, Y.",
  title		= "Semidefinite programming based algorithms for sensor
		  network localization",
  journal	= "ACM Transactions on Sensor Networks",
  year		= 2006,
  volume	= 2,
  page		= {188--220}
}

###Article{	  blwy06,
  author	= "Biswas, P. and Liang, T.-C. and Wang, T.-C. and Ye, Y.",
  title		= "Semidefinite programming based algorithms for sensor
		  network localization",
  journal	= "ACM Transactions on Sensor Networks",
  year		= 2006,
  volume	= 2,
  page		= {188--220}
}

###Article{	  blwy06,
  author	= "Biswas, P. and Liang, T.-C. and Wang, T.-C. and Ye, Y.",
  title		= "Semidefinite programming based algorithms for sensor
		  network localization",
  journal	= "ACM Transactions on Sensor Networks",
  year		= 2006,
  volume	= 2,
  page		= {188--220}
}

@InCollection{	  boispozoremibarrdong:1997,
  address	= {London},
  author	= {R. Boisvert and R. Pozo and K. Remington and R. Barrett
		  and J. Dongarra},
  booktitle	= {The quality of numerical software: assessment and
		  enhancement},
  editor	= {R. F. Boisvert},
  pages		= {125--137},
  publisher	= {Chapman \& Hall},
  title		= {{Matrix Market}: {A} web resource for test matrix
		  collections},
  year		= 1997
}

###InCollection{  boispozoremibarrdong:1997,
  address	= {London},
  author	= {R. Boisvert and R. Pozo and K. Remington and R. Barrett
		  and J. Dongarra},
  booktitle	= {The quality of numerical software: assessment and
		  enhancement},
  editor	= {R. F. Boisvert},
  pages		= {125--137},
  publisher	= {Chapman \& Hall},
  title		= {{Matrix Market}: {A} web resource for test matrix
		  collections},
  year		= 1997
}

###InCollection{  boispozoremibarrdong:1997,
  address	= {London},
  author	= {R. Boisvert and R. Pozo and K. Remington and R. Barrett
		  and J. Dongarra},
  booktitle	= {The quality of numerical software: assessment and
		  enhancement},
  editor	= {R. F. Boisvert},
  pages		= {125--137},
  publisher	= {Chapman \& Hall},
  title		= {{Matrix Market}: {A} web resource for test matrix
		  collections},
  year		= 1997
}

@Article{	  bongconngoultoin:1995,
  author	= {I. Bongartz and A. R. Conn and N. I. M. Gould and
		  {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= acmmathsoft,
  month		= {March},
  number	= 1,
  pages		= {123-160},
  title		= {{CUTE}: Constrained and Unconstrained Testing
		  Environment},
  volume	= 21,
  year		= 1995
}

###Article{	  bongconngoultoin:1995,
  author	= {I. Bongartz and A. R. Conn and N. I. M. Gould and
		  {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= acmmathsoft,
  month		= {March},
  number	= 1,
  pages		= {123-160},
  title		= {{CUTE}: Constrained and Unconstrained Testing
		  Environment},
  volume	= 21,
  year		= 1995
}

###Article{	  bongconngoultoin:1995,
  author	= {I. Bongartz and A. R. Conn and N. I. M. Gould and
		  {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= acmmathsoft,
  month		= {March},
  number	= 1,
  pages		= {123-160},
  title		= {{CUTE}: Constrained and Unconstrained Testing
		  Environment},
  volume	= 21,
  year		= 1995
}

@Book{		  bonnans2006numerical,
  title		= {Numerical optimization: theoretical and practical
		  aspects},
  author	= {Bonnans, Joseph-Fr{\'e}d{\'e}ric and Gilbert, Jean Charles
		  and Lemar{\'e}chal, Claude and Sagastiz{\'a}bal, Claudia A},
  year		= 2006,
  publisher	= {Springer}
}

###Book{	  bonnans2006numerical,
  title		= {Numerical optimization: theoretical and practical
		  aspects},
  author	= {Bonnans, Joseph-Fr{\'e}d{\'e}ric and Gilbert, Jean Charles
		  and Lemar{\'e}chal, Claude and Sagastiz{\'a}bal, Claudia A},
  year		= 2006,
  publisher	= {Springer}
}

@InProceedings{	  bottou-2010,
  author	= {Bottou, L\'{e}on},
  title		= {Large-Scale Machine Learning with Stochastic Gradient
		  Descent},
  year		= 2010,
  booktitle	= {Proceedings of the 19th International Conference on
		  Computational Statistics (COMPSTAT'2010)},
  editor	= {Lechevallier, Yves and Saporta, Gilbert},
  address	= {Paris, France},
  month		= {August},
  publisher	= {Springer},
  pages		= {177--187},
  url		= {http://leon.bottou.org/papers/bottou-2010}
}

###InProceedings{ bottou-2010,
  author	= {Bottou, L\'{e}on},
  title		= {Large-Scale Machine Learning with Stochastic Gradient
		  Descent},
  year		= 2010,
  booktitle	= {Proceedings of the 19th International Conference on
		  Computational Statistics (COMPSTAT'2010)},
  editor	= {Lechevallier, Yves and Saporta, Gilbert},
  address	= {Paris, France},
  month		= {August},
  publisher	= {Springer},
  pages		= {177--187},
  url		= {http://leon.bottou.org/papers/bottou-2010}
}

###InProceedings{ bottou-2010,
  author	= {Bottou, L\'{e}on},
  title		= {Large-Scale Machine Learning with Stochastic Gradient
		  Descent},
  year		= 2010,
  booktitle	= {Proceedings of the 19th International Conference on
		  Computational Statistics (COMPSTAT'2010)},
  editor	= {Lechevallier, Yves and Saporta, Gilbert},
  address	= {Paris, France},
  month		= {August},
  publisher	= {Springer},
  pages		= {177--187},
  url		= {http://leon.bottou.org/papers/bottou-2010}
}

@Book{		  boucheron:2013,
  title		= {Concentration Inequalities: A Nonasymptotic Theory of
		  Independence},
  author	= {Boucheron, St{\'e}phane and Lugosi, G{\'a}bor and Massart,
		  Pascal},
  year		= {2013},
  publisher	= {Oxford University Press}
}

@Article{	  boyd2007localization,
  title		= {Localization and cutting-plane methods},
  author	= {Boyd, Stephen and Vandenberghe, Lieven},
  journal	= {From Stanford EE 364b lecture notes},
  year		= {2007}
}

@Article{	  boyd2011distributed,
  title		= {Distributed optimization and statistical learning via the
		  alternating direction method of multipliers},
  author	= {Boyd, Stephen and Parikh, Neal and Chu, Eric and Peleato,
		  Borja and Eckstein, Jonathan},
  journal	= {Foundations and Trends in Machine Learning},
  volume	= 3,
  number	= 1,
  pages		= {1--122},
  year		= 2011,
  publisher	= {Now Publishers Inc.}
}

###Article{	  boyd2011distributed,
  title		= {Distributed optimization and statistical learning via the
		  alternating direction method of multipliers},
  author	= {Boyd, Stephen and Parikh, Neal and Chu, Eric and Peleato,
		  Borja and Eckstein, Jonathan},
  journal	= {Foundations and Trends in Machine Learning},
  volume	= 3,
  number	= 1,
  pages		= {1--122},
  year		= 2011,
  publisher	= {Now Publishers Inc.}
}

@Book{		  boyd:2004,
  author	= {Boyd, Stephen and Vandenberghe, Lieven},
  title		= {Convex optimization},
  publisher	= {Cambridge University Press, Cambridge},
  year		= 2004,
  pages		= {xiv+716},
  doi		= {10.1017/CBO9780511804441}
}

###Book{	  boyd:2004,
  author	= {Boyd, Stephen and Vandenberghe, Lieven},
  title		= {Convex optimization},
  publisher	= {Cambridge University Press, Cambridge},
  year		= 2004,
  pages		= {xiv+716},
  doi		= {10.1017/CBO9780511804441}
}

@Book{		  boydvand:2004,
  address	= {Cambridge, UK},
  author	= {Stephen Boyd and Lieven Vandenberghe},
  date-modified	= {2007-07-26 16:36:59 -0700},
  publisher	= cambridgepress,
  title		= {Convex Optimization},
  year		= 2004
}

###Book{	  boydvand:2004,
  address	= {Cambridge, UK},
  author	= {Stephen Boyd and Lieven Vandenberghe},
  date-modified	= {2007-07-26 16:36:59 -0700},
  publisher	= cambridgepress,
  title		= {Convex Optimization},
  year		= 2004
}

###Book{	  boydvand:2004,
  address	= {Cambridge, UK},
  author	= {Stephen Boyd and Lieven Vandenberghe},
  date-modified	= {2007-07-26 16:36:59 -0700},
  publisher	= cambridgepress,
  title		= {Convex Optimization},
  year		= 2004
}

@TechReport{	  bran:1995,
  address	= {Cornell Theory Center, Cornell University},
  author	= {M. A. Branch},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Advanced Computing Research Institute},
  month		= {September},
  number	= {CTC94TR194},
  title		= {Getting {CUTE} with \textsc{Matlab}},
  year		= 1995
}

###TechReport{	  bran:1995,
  address	= {Cornell Theory Center, Cornell University},
  author	= {M. A. Branch},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Advanced Computing Research Institute},
  month		= {September},
  number	= {CTC94TR194},
  title		= {Getting {CUTE} with \textsc{Matlab}},
  year		= 1995
}

###TechReport{	  bran:1995,
  address	= {Cornell Theory Center, Cornell University},
  author	= {M. A. Branch},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Advanced Computing Research Institute},
  month		= {September},
  number	= {CTC94TR194},
  title		= {Getting {CUTE} with \textsc{Matlab}},
  year		= 1995
}

@InProceedings{	  brau:1977,
  address	= {Berlin, New York},
  author	= {J{\"u}rgen Br{\"a}uninger},
  booktitle	= {Optimization Techniques, proceedings of the 8th {IFIP}
		  Conference, W{\"u}rzburg, Part 2, Lecture Notes in Control
		  and Inform. Sci.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  pages		= {33-41},
  publisher	= springer,
  title		= {A Modification of {R}obinson's Algorithm for General
		  Nonlinear Programming Problems Requiring Only Approximate
		  Solutions of Subproblems with Linear Equality Constraints},
  year		= 1977
}

###InProceedings{ brau:1977,
  address	= {Berlin, New York},
  author	= {J{\"u}rgen Br{\"a}uninger},
  booktitle	= {Optimization Techniques, proceedings of the 8th {IFIP}
		  Conference, W{\"u}rzburg, Part 2, Lecture Notes in Control
		  and Inform. Sci.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  pages		= {33-41},
  publisher	= springer,
  title		= {A Modification of {R}obinson's Algorithm for General
		  Nonlinear Programming Problems Requiring Only Approximate
		  Solutions of Subproblems with Linear Equality Constraints},
  year		= 1977
}

###InProceedings{ brau:1977,
  address	= {Berlin, New York},
  author	= {J{\"u}rgen Br{\"a}uninger},
  booktitle	= {Optimization Techniques, proceedings of the 8th {IFIP}
		  Conference, W{\"u}rzburg, Part 2, Lecture Notes in Control
		  and Inform. Sci.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  pages		= {33-41},
  publisher	= springer,
  title		= {A Modification of {R}obinson's Algorithm for General
		  Nonlinear Programming Problems Requiring Only Approximate
		  Solutions of Subproblems with Linear Equality Constraints},
  year		= 1977
}

@Article{	  brau:1981,
  author	= {J{\"u}rgen Br{\"a}uninger},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= jota,
  month		= {October},
  number	= 2,
  pages		= {195-216},
  title		= {A Globally Convergent Version of {R}obinson's Algorithm
		  for General Nonlinear Programming Problems Without Using
		  Derivatives},
  volume	= 35,
  year		= 1981
}

###Article{	  brau:1981,
  author	= {J{\"u}rgen Br{\"a}uninger},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= jota,
  month		= {October},
  number	= 2,
  pages		= {195-216},
  title		= {A Globally Convergent Version of {R}obinson's Algorithm
		  for General Nonlinear Programming Problems Without Using
		  Derivatives},
  volume	= 35,
  year		= 1981
}

###Article{	  brau:1981,
  author	= {J{\"u}rgen Br{\"a}uninger},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= jota,
  month		= {October},
  number	= 2,
  pages		= {195-216},
  title		= {A Globally Convergent Version of {R}obinson's Algorithm
		  for General Nonlinear Programming Problems Without Using
		  Derivatives},
  volume	= 35,
  year		= 1981
}

@Article{	  bre67,
  author	= "L.M. Bregman",
  title		= "The Relaxation Method of Finding the Common Point of
		  Convex Sets and Its Application to the Solution of Problems
		  in Convex Programming ",
  journal	= "USSR Computational Mathematics and Mathematical Physics",
  year		= 1967,
  volume	= 7,
  page		= {200--217}
}

###Article{	  bre67,
  author	= "L.M. Bregman",
  title		= "The Relaxation Method of Finding the Common Point of
		  Convex Sets and Its Application to the Solution of Problems
		  in Convex Programming ",
  journal	= "USSR Computational Mathematics and Mathematical Physics",
  year		= 1967,
  volume	= 7,
  page		= {200--217}
}

###Article{	  bre67,
  author	= "L.M. Bregman",
  title		= "The Relaxation Method of Finding the Common Point of
		  Convex Sets and Its Application to the Solution of Problems
		  in Convex Programming ",
  journal	= "USSR Computational Mathematics and Mathematical Physics",
  year		= 1967,
  volume	= 7,
  page		= {200--217}
}

@Article{	  bren:1972,
  author	= {R. P. Brent},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {IBM J. Research and Development},
  note		= {email him for ref},
  title		= {On the Davidenko-Branin method for solving simultaneous
		  nonlinear equations},
  year		= 1972
}

###Article{	  bren:1972,
  author	= {R. P. Brent},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {IBM J. Research and Development},
  note		= {email him for ref},
  title		= {On the Davidenko-Branin method for solving simultaneous
		  nonlinear equations},
  year		= 1972
}

###Article{	  bren:1972,
  author	= {R. P. Brent},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {IBM J. Research and Development},
  note		= {email him for ref},
  title		= {On the Davidenko-Branin method for solving simultaneous
		  nonlinear equations},
  year		= 1972
}

@Article{	  brenwinowolf:1973,
  author	= {R. P. Brent and S. Winograd and P. Wolfe},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= numermath,
  pages		= {327-341},
  title		= {Optimal iterative processes for rootfinding},
  year		= 1973
}

###Article{	  brenwinowolf:1973,
  author	= {R. P. Brent and S. Winograd and P. Wolfe},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= numermath,
  pages		= {327-341},
  title		= {Optimal iterative processes for rootfinding},
  year		= 1973
}

###Article{	  brenwinowolf:1973,
  author	= {R. P. Brent and S. Winograd and P. Wolfe},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= numermath,
  pages		= {327-341},
  title		= {Optimal iterative processes for rootfinding},
  year		= 1973
}

@Article{	  brodgourgree:1973,
  author	= {K. W. Brodlie and A. R. Gourlay and J. Greenstadt},
  journal	= instmathapp,
  pages		= {73--82},
  title		= {Rank-one and rank-two corrections to positive definite
		  matrices expressed in product form},
  volume	= 11,
  number	= 1,
  year		= 1973
}

###Article{	  brodgourgree:1973,
  author	= {K. W. Brodlie and A. R. Gourlay and J. Greenstadt},
  journal	= instmathapp,
  pages		= {73--82},
  title		= {Rank-one and rank-two corrections to positive definite
		  matrices expressed in product form},
  volume	= 11,
  number	= 1,
  year		= 1973
}

###Article{	  brodgourgree:1973,
  author	= {K. W. Brodlie and A. R. Gourlay and J. Greenstadt},
  journal	= instmathapp,
  pages		= {73--82},
  title		= {Rank-one and rank-two corrections to positive definite
		  matrices expressed in product form},
  volume	= 11,
  number	= 1,
  year		= 1973
}

@Book{		  brookendmeer:1988,
  address	= {Redwood City, CA},
  author	= {A. Brooke and D. Kendrick and A. Meeraus},
  date-modified	= {2007-07-18 14:59:32 -0700},
  publisher	= scientific,
  title		= {{GAMS}: A User's Guide},
  year		= 1988
}

###Book{	  brookendmeer:1988,
  address	= {Redwood City, CA},
  author	= {A. Brooke and D. Kendrick and A. Meeraus},
  date-modified	= {2007-07-18 14:59:32 -0700},
  publisher	= scientific,
  title		= {{GAMS}: A User's Guide},
  year		= 1988
}

###Book{	  brookendmeer:1988,
  address	= {Redwood City, CA},
  author	= {A. Brooke and D. Kendrick and A. Meeraus},
  date-modified	= {2007-07-18 14:59:32 -0700},
  publisher	= scientific,
  title		= {{GAMS}: A User's Guide},
  year		= 1988
}

@Article{	  bt:2000,
  author	= {Dimitri P. Bertsekas and John N. Tsitsiklis},
  title		= {Gradient convergence in gradient methods with errors},
  journal	= siamopt,
  year		= 2000,
  volume	= 10,
  number	= 3,
  pages		= {627-642}
}

###Article{	  bt:2000,
  author	= {Dimitri P. Bertsekas and John N. Tsitsiklis},
  title		= {Gradient convergence in gradient methods with errors},
  journal	= siamoptim,
  year		= 2000,
  volume	= 10,
  number	= 3,
  pages		= {627-642}
}

###Article{	  bt:2000,
  author	= {Dimitri P. Bertsekas and John N. Tsitsiklis},
  title		= {Gradient convergence in gradient methods with errors},
  journal	= siamoptim,
  year		= 2000,
  volume	= 10,
  number	= 3,
  pages		= {627-642}
}

@Article{	  bty08,
  author	= "Biswas, P. and Toh, K.-C. and Ye, Y.",
  title		= "A Distributed {SDP} Approach for Large-Scale Noisy
		  Anchor-Free Graph Realization with Applications to
		  Molecular Conformation",
  journal	= siamcomp,
  year		= 2008,
  volume	= 30,
  number	= 2,
  page		= {1251--1277}
}

###Article{	  bty08,
  author	= "Biswas, P. and Toh, K.-C. and Ye, Y.",
  title		= "A Distributed {SDP} Approach for Large-Scale Noisy
		  Anchor-Free Graph Realization with Applications to
		  Molecular Conformation",
  journal	= siamcomp,
  year		= 2008,
  volume	= 30,
  number	= 2,
  page		= {1251--1277}
}

###Article{	  bty08,
  author	= "Biswas, P. and Toh, K.-C. and Ye, Y.",
  title		= "A Distributed {SDP} Approach for Large-Scale Noisy
		  Anchor-Free Graph Realization with Applications to
		  Molecular Conformation",
  journal	= siamcomp,
  year		= 2008,
  volume	= 30,
  number	= 2,
  page		= {1251--1277}
}

@Article{	  buck85,
  author	= {A. Buckley and A. {LeNir}},
  title		= {{BBVSCG}—a variable storage algorithm for function
		  minimization},
  journal	= acmmathsoft,
  number	= 11,
  year		= 1985,
  pages		= {103–-119}
}

###Article{	  buck85,
  author	= {A. Buckley and A. {LeNir}},
  title		= {{BBVSCG}—a variable storage algorithm for function
		  minimization},
  journal	= acmmathsoft,
  number	= 11,
  year		= 1985,
  pages		= {103–-119}
}

###Article{	  buck85,
  author	= {A. Buckley and A. {LeNir}},
  title		= {{BBVSCG}—a variable storage algorithm for function
		  minimization},
  journal	= acmmathsoft,
  number	= 11,
  year		= 1985,
  pages		= {103–-119}
}

@InCollection{	  buckdono:1995,
  address	= {New York},
  author	= {J. Buckheit and D. L. Donoho},
  booktitle	= {Wavelets and Statistics},
  editor	= {A. Anatoniadis},
  publisher	= {Springer-Verlag},
  title		= {{WaveLab} and reproducible research},
  year		= 1995
}

###InCollection{  buckdono:1995,
  address	= {New York},
  author	= {J. Buckheit and D. L. Donoho},
  booktitle	= {Wavelets and Statistics},
  editor	= {A. Anatoniadis},
  publisher	= {Springer-Verlag},
  title		= {{WaveLab} and reproducible research},
  year		= 1995
}

###InCollection{  buckdono:1995,
  address	= {New York},
  author	= {J. Buckheit and D. L. Donoho},
  booktitle	= {Wavelets and Statistics},
  editor	= {A. Anatoniadis},
  publisher	= {Springer-Verlag},
  title		= {{WaveLab} and reproducible research},
  year		= 1995
}

@Article{	  buckl83,
  author	= {A. Buckley and A. {LeNir}},
  title		= {{QN-like} variable storage conjugate gradients},
  journal	= mathprog,
  number	= 27,
  year		= 1983,
  pages		= {155-–175}
}

###Article{	  buckl83,
  author	= {A. Buckley and A. {LeNir}},
  title		= {{QN-like} variable storage conjugate gradients},
  journal	= mathprog,
  number	= 27,
  year		= 1983,
  pages		= {155-–175}
}

###Article{	  buckl83,
  author	= {A. Buckley and A. {LeNir}},
  title		= {{QN-like} variable storage conjugate gradients},
  journal	= mathprog,
  number	= 27,
  year		= 1983,
  pages		= {155-–175}
}

@Article{	  bure:2009,
  title		= {Optimizing a polyhedral-semidefinite relaxation of
		  completely positive programs},
  author	= {Sam Burer},
  journal	= {Journal Mathematical Programming Computation},
  volume	= 2,
  number	= 1,
  month		= {March},
  year		= 2010,
  doi		= {10.1007/s12532-010-0010-8},
  pages		= {1-19}
}

###Article{	  bure:2009,
  title		= {Optimizing a polyhedral-semidefinite relaxation of
		  completely positive programs},
  author	= {Sam Burer},
  journal	= {Journal Mathematical Programming Computation},
  volume	= 2,
  number	= 1,
  month		= {March},
  year		= 2010,
  doi		= {10.1007/s12532-010-0010-8},
  pages		= {1-19}
}

###Article{	  bure:2009,
  title		= {Optimizing a polyhedral-semidefinite relaxation of
		  completely positive programs},
  author	= {Sam Burer},
  journal	= {Journal Mathematical Programming Computation},
  volume	= 2,
  number	= 1,
  month		= {March},
  year		= 2010,
  doi		= {10.1007/s12532-010-0010-8},
  pages		= {1-19}
}

@Article{	  burer:2002,
  author	= {Burer, Samuel and Monteiro, Renato D. C. and Zhang, Yin},
  title		= {Solving a class of semidefinite programs via nonlinear
		  programming},
  journal	= mathprog,
  volume	= 93,
  year		= 2002,
  number	= {1},
  pages		= {97--122},
  doi		= {10.1007/s101070100279}
}

###Article{	  burer:2002,
  author	= {Burer, Samuel and Monteiro, Renato D. C. and Zhang, Yin},
  title		= {Solving a class of semidefinite programs via nonlinear
		  programming},
  journal	= mathprog,
  volume	= 93,
  year		= 2002,
  number	= {1},
  pages		= {97--122},
  doi		= {10.1007/s101070100279}
}

@Article{	  burer:2005,
  author	= {Burer, Samuel and Monteiro, Renato D. C.},
  title		= {Local minima and convergence in low-rank semidefinite
		  programming},
  journal	= mathprog,
  volume	= 103,
  year		= 2005,
  number	= {3},
  pages		= {427--444},
  doi		= {10.1007/s10107-004-0564-1}
}

###Article{	  burer:2005,
  author	= {Burer, Samuel and Monteiro, Renato D. C.},
  title		= {Local minima and convergence in low-rank semidefinite
		  programming},
  journal	= mathprog,
  volume	= 103,
  year		= 2005,
  number	= {3},
  pages		= {427--444},
  doi		= {10.1007/s10107-004-0564-1}
}

@Article{	  burermont:2003,
  author	= {Burer, Samuel and Monteiro, Renato D.C.},
  affiliation	= {Department of Management Sciences, University of Iowa,
		  Iowa City, IA 52242-1000, e-mail: burer@math.gatech.edu
		  US},
  title		= {A nonlinear programming algorithm for solving semidefinite
		  programs via low-rank factorization},
  journal	= mathprog,
  pages		= {329-357},
  volume	= 95,
  issue		= 2,
  doi		= {10.1007/s10107-002-0352-8},
  abstract	= {In this paper, we present a nonlinear programming
		  algorithm for solving semidefinite programs (SDPs) in
		  standard form. The algorithm's distinguishing feature is a
		  change of variables that replaces the symmetric, positive
		  semidefinite variable X of the SDP with a rectangular
		  variable R according to the factorization X = RR T . The
		  rank of the factorization, i.e., the number of columns of R
		  , is chosen minimally so as to enhance computational speed
		  while maintaining equivalence with the SDP. Fundamental
		  results concerning the convergence of the algorithm are
		  derived, and encouraging computational results on some
		  large-scale test problems are also presented.},
  year		= 2003
}


@Article{	  burk:1991,
  author	= {J. V. Burke},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamcontrol,
  number	= 4,
  pages		= {968--998},
  title		= {An exact penalization viewpoint of constrained
		  optimization},
  volume	= 29,
  year		= 1991
}

###Article{	  burk:1991,
  author	= {J. V. Burke},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamcontrol,
  number	= 4,
  pages		= {968--998},
  title		= {An exact penalization viewpoint of constrained
		  optimization},
  volume	= 29,
  year		= 1991
}

###Article{	  burk:1991,
  author	= {J. V. Burke},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamcontrol,
  number	= 4,
  pages		= {968--998},
  title		= {An exact penalization viewpoint of constrained
		  optimization},
  volume	= 29,
  year		= 1991
}

@Article{	  burkdeng:2005,
  author	= {J. V. Burke and S. Deng},
  date-modified	= {2007-07-18 14:59:34 -0700},
  issn		= {0025-5610},
  journal	= mathprog,
  mrclass	= {90C31 (49J52)},
  mrnumber	= {MR2179237 (2006e:90125)},
  number	= {2-3, Ser. B},
  pages		= {235--261},
  title		= {Weak sharp minima revisited. {II}. {A}pplication to linear
		  regularity and error bounds},
  volume	= 104,
  year		= 2005
}

###Article{	  burkdeng:2005,
  author	= {J. V. Burke and S. Deng},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathprog,
  mrclass	= {90C31 (49J52)},
  mrnumber	= {MR2179237 (2006e:90125)},
  number	= {2-3, Ser. B},
  pages		= {235--261},
  title		= {Weak sharp minima revisited. {II}. {A}pplication to linear
		  regularity and error bounds},
  volume	= 104,
  year		= 2005
}

###Article{	  burkdeng:2005,
  author	= {J. V. Burke and S. Deng},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathprog,
  mrclass	= {90C31 (49J52)},
  mrnumber	= {MR2179237 (2006e:90125)},
  number	= {2-3, Ser. B},
  pages		= {235--261},
  title		= {Weak sharp minima revisited. {II}. {A}pplication to linear
		  regularity and error bounds},
  volume	= 104,
  year		= 2005
}

@Article{	  burkehoheisel:2013,
  author	= {James V. Burke and Tim Hoheisel},
  title		= {Epi-convergent Smoothing with Applications to Convex
		  Composite Functions},
  journal	= siamopt,
  volume	= 23,
  number	= 3,
  pages		= {1457-1479},
  year		= 2013
}

###Article{	  burkehoheisel:2013,
  author	= {James V. Burke and Tim Hoheisel},
  title		= {Epi-convergent Smoothing with Applications to Convex
		  Composite Functions},
  journal	= siamopt,
  volume	= 23,
  number	= 3,
  pages		= {1457-1479},
  year		= 2013
}

@Article{	  burkferr:1993,
  author	= {J. V. Burke and M. C. Ferris},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamcontrol,
  number	= 5,
  pages		= {1340--1359},
  title		= {Weak sharp minima in mathematical programming},
  volume	= 31,
  year		= 1993
}

###Article{	  burkferr:1993,
  author	= {J. V. Burke and M. C. Ferris},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamcontrol,
  number	= 5,
  pages		= {1340--1359},
  title		= {Weak sharp minima in mathematical programming},
  volume	= 31,
  year		= 1993
}

###Article{	  burkferr:1993,
  author	= {J. V. Burke and M. C. Ferris},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamcontrol,
  number	= 5,
  pages		= {1340--1359},
  title		= {Weak sharp minima in mathematical programming},
  volume	= 31,
  year		= 1993
}

@Article{	  burkhan:1986,
  author	= {J. Burke and {S.-P.} Han},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathofor,
  month		= {November},
  number	= 4,
  pages		= {632-643},
  title		= {A {G}auss-{N}ewton Approach to Solving Generalized
		  Inequalities},
  volume	= 11,
  year		= 1986
}

###Article{	  burkhan:1986,
  author	= {J. Burke and {S.-P.} Han},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathofor,
  month		= {November},
  number	= 4,
  pages		= {632-643},
  title		= {A {G}auss-{N}ewton Approach to Solving Generalized
		  Inequalities},
  volume	= 11,
  year		= 1986
}

###Article{	  burkhan:1986,
  author	= {J. Burke and {S.-P.} Han},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathofor,
  month		= {November},
  number	= 4,
  pages		= {632-643},
  title		= {A {G}auss-{N}ewton Approach to Solving Generalized
		  Inequalities},
  volume	= 11,
  year		= 1986
}

@Article{	  burkmore:1994,
  author	= {James V. Burke and Jorge J. Mor\'{e}},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {573--595},
  title		= {Exposing constraints},
  volume	= 4,
  year		= 1994
}

###Article{	  burkmore:1994,
  author	= {James V. Burke and Jorge J. Mor\'{e}},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {573--595},
  title		= {Exposing constraints},
  volume	= 4,
  year		= 1994
}

###Article{	  burkmore:1994,
  author	= {James V. Burke and Jorge J. Mor\'{e}},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {573--595},
  title		= {Exposing constraints},
  volume	= 4,
  year		= 1994
}

@TechReport{	  buso:1985,
  title		= {Handling degeneracy in a nonlinear L1 algorithm},
  author	= {Busovaca, S.},
  institution	= {Computer Science Department, University of Waterloo},
  type		= {Tech. Rep.},
  number	= {CS-85-34},
  year		= 1985
}

###TechReport{	  buso:1985,
  title		= {Handling degeneracy in a nonlinear L1 algorithm},
  author	= {Busovaca, S.},
  institution	= {Computer Science Department, University of Waterloo},
  type		= {Tech. Rep.},
  number	= {CS-85-34},
  year		= 1985
}

###TechReport{	  buso:1985,
  title		= {Handling degeneracy in a nonlinear L1 algorithm},
  author	= {Busovaca, S.},
  institution	= {Computer Science Department, University of Waterloo},
  type		= {Tech. Rep.},
  number	= {CS-85-34},
  year		= 1985
}

@Article{	  byrd2013inexact,
  title		= {An inexact successive quadratic approximation method for
		  convex L-1 regularized optimization},
  author	= {Byrd, Richard H and Nocedal, Jorge and Oztoprak, Figen},
  journal	= mathprog,
  year		= 2015
}

###Article{	  byrd2013inexact,
  title		= {An inexact successive quadratic approximation method for
		  convex L-1 regularized optimization},
  author	= {Byrd, Richard H and Nocedal, Jorge and Oztoprak, Figen},
  journal	= {Mathematical Programming B},
  year		= {2015}
}

@Article{	  byrd2016,
  author	= "Byrd, Richard H. and Nocedal, Jorge and Oztoprak, Figen",
  title		= "An inexact successive quadratic approximation method for
		  {L-1} regularized optimization",
  journal	= mathprog,
  year		= 2016,
  volume	= 157,
  number	= 2,
  pages		= "375--396",
  abstract	= "We study a Newton-like method for the minimization of an
		  objective function {\$}{\$}{\backslash}phi {\$}{\$} ϕ that
		  is the sum of a smooth function and an
		  {\$}{\$}{\backslash}ell {\_}1{\$}{\$} ℓ 1 regularization
		  term. This method, which is sometimes referred to in the
		  literature as a proximal Newton method, computes a step by
		  minimizing a piecewise quadratic model
		  {\$}{\$}q{\_}k{\$}{\$} q k of the objective function
		  {\$}{\$}{\backslash}phi {\$}{\$} ϕ . In order to make this
		  approach efficient in practice, it is imperative to perform
		  this inner minimization inexactly. In this paper, we give
		  inexactness conditions that guarantee global convergence
		  and that can be used to control the local rate of
		  convergence of the iteration. Our inexactness conditions
		  are based on a semi-smooth function that represents a
		  (continuous) measure of the optimality conditions of the
		  problem, and that embodies the soft-thresholding iteration.
		  We give careful consideration to the algorithm employed for
		  the inner minimization, and report numerical results on two
		  test sets originating in machine learning."
}

@Article{	  byrd:2011,
  author	= {Richard H. Byrd and Gillian M. Chin and Will Neveitt and
		  Jorge Nocedal},
  title		= {On the Use of Stochastic Hessian Information in
		  Optimization Methods for Machine Learning},
  publisher	= {SIAM},
  year		= 2011,
  journal	= siamopt,
  volume	= 21,
  number	= 3,
  pages		= {977-995},
  keywords	= {unconstrained optimization; stochastic optimization;
		  machine learning},
  url		= {http://link.aip.org/link/?SJE/21/977/1},
  doi		= {10.1137/10079923X}
}

###Article{	  byrd:2011,
  author	= {Richard H. Byrd and Gillian M. Chin and Will Neveitt and
		  Jorge Nocedal},
  title		= {On the Use of Stochastic Hessian Information in
		  Optimization Methods for Machine Learning},
  publisher	= {SIAM},
  year		= 2011,
  journal	= siamopt,
  volume	= 21,
  number	= 3,
  pages		= {977-995},
  keywords	= {unconstrained optimization; stochastic optimization;
		  machine learning},
  url		= {http://link.aip.org/link/?SJE/21/977/1},
  doi		= {10.1137/10079923X}
}

###Article{	  byrd:2011,
  author	= {Richard H. Byrd and Gillian M. Chin and Will Neveitt and
		  Jorge Nocedal},
  title		= {On the Use of Stochastic Hessian Information in
		  Optimization Methods for Machine Learning},
  publisher	= {SIAM},
  year		= 2011,
  journal	= siamopt,
  volume	= 21,
  number	= 3,
  pages		= {977-995},
  keywords	= {unconstrained optimization; stochastic optimization;
		  machine learning},
  url		= {http://link.aip.org/link/?SJE/21/977/1},
  doi		= {10.1137/10079923X}
}

@Article{	  byrd:2012,
  author	= {Byrd, Richard H. and Chin, Gillian and Nocedal, Jorge and
		  Wu, Yuchen},
  affiliation	= {Department of Computer Science, University of Colorado,
		  Boulder, CO, USA},
  title		= {Sample size selection in optimization methods for machine
		  learning},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  issn		= {0025-5610},
  keyword	= {Mathematics and Statistics},
  pages		= {127-155},
  volume	= 134,
  issue		= 1,
  doi		= {10.1007/s10107-012-0572-5},
  abstract	= {This paper presents a methodology for using varying sample
		  sizes in batch-type optimization methods for large-scale
		  machine learning problems. The first part of the paper
		  deals with the delicate issue of dynamic sample selection
		  in the evaluation of the function and gradient. We propose
		  a criterion for increasing the sample size based on
		  variance estimates obtained during the computation of a
		  batch gradient. We establish an $${O(1/\epsilon)}$$
		  complexity bound on the total cost of a gradient method.
		  The second part of the paper describes a practical Newton
		  method that uses a smaller sample to compute Hessian
		  vector-products than to evaluate the function and the
		  gradient, and that also employs a dynamic sampling
		  technique. The focus of the paper shifts in the third part
		  of the paper to L 1 -regularized problems designed to
		  produce sparse solutions. We propose a Newton-like method
		  that consists of two phases: a (minimalistic) gradient
		  projection phase that identifies zero variables, and
		  subspace phase that applies a subsampled Hessian Newton
		  iteration in the free variables. Numerical tests on speech
		  recognition problems illustrate the performance of the
		  algorithms.},
  year		= 2012
}

###Article{	  byrd:2012,
  author	= {Byrd, Richard H. and Chin, Gillian and Nocedal, Jorge and
		  Wu, Yuchen},
  affiliation	= {Department of Computer Science, University of Colorado,
		  Boulder, CO, USA},
  title		= {Sample size selection in optimization methods for machine
		  learning},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {127-155},
  volume	= 134,
  issue		= 1,
  doi		= {10.1007/s10107-012-0572-5},
  abstract	= {This paper presents a methodology for using varying sample
		  sizes in batch-type optimization methods for large-scale
		  machine learning problems. The first part of the paper
		  deals with the delicate issue of dynamic sample selection
		  in the evaluation of the function and gradient. We propose
		  a criterion for increasing the sample size based on
		  variance estimates obtained during the computation of a
		  batch gradient. We establish an $${O(1/\epsilon)}$$
		  complexity bound on the total cost of a gradient method.
		  The second part of the paper describes a practical Newton
		  method that uses a smaller sample to compute Hessian
		  vector-products than to evaluate the function and the
		  gradient, and that also employs a dynamic sampling
		  technique. The focus of the paper shifts in the third part
		  of the paper to L 1 -regularized problems designed to
		  produce sparse solutions. We propose a Newton-like method
		  that consists of two phases: a (minimalistic) gradient
		  projection phase that identifies zero variables, and
		  subspace phase that applies a subsampled Hessian Newton
		  iteration in the free variables. Numerical tests on speech
		  recognition problems illustrate the performance of the
		  algorithms.},
  year		= 2012
}

###Article{	  byrd:2012,
  author	= {Byrd, Richard H. and Chin, Gillian and Nocedal, Jorge and
		  Wu, Yuchen},
  affiliation	= {Department of Computer Science, University of Colorado,
		  Boulder, CO, USA},
  title		= {Sample size selection in optimization methods for machine
		  learning},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {127-155},
  volume	= 134,
  issue		= 1,
  doi		= {10.1007/s10107-012-0572-5},
  abstract	= {This paper presents a methodology for using varying sample
		  sizes in batch-type optimization methods for large-scale
		  machine learning problems. The first part of the paper
		  deals with the delicate issue of dynamic sample selection
		  in the evaluation of the function and gradient. We propose
		  a criterion for increasing the sample size based on
		  variance estimates obtained during the computation of a
		  batch gradient. We establish an $${O(1/\epsilon)}$$
		  complexity bound on the total cost of a gradient method.
		  The second part of the paper describes a practical Newton
		  method that uses a smaller sample to compute Hessian
		  vector-products than to evaluate the function and the
		  gradient, and that also employs a dynamic sampling
		  technique. The focus of the paper shifts in the third part
		  of the paper to L 1 -regularized problems designed to
		  produce sparse solutions. We propose a Newton-like method
		  that consists of two phases: a (minimalistic) gradient
		  projection phase that identifies zero variables, and
		  subspace phase that applies a subsampled Hessian Newton
		  iteration in the free variables. Numerical tests on speech
		  recognition problems illustrate the performance of the
		  algorithms.},
  year		= 2012
}

@Article{	  byrdgoulnocewalt:2004,
  author	= {R. H. Byrd and N. I. M. Gould and J. Nocedal and R. A.
		  Waltz},
  date-modified	= {2007-10-22 16:57:18 -0700},
  journal	= mathprog,
  number	= 1,
  pages		= {27--48},
  title		= {An algorithm for nonlinear optimization using linear
		  programming and equality constrained subproblems},
  volume	= 100,
  year		= 2004
}

###Article{	  byrdgoulnocewalt:2004,
  author	= {R. H. Byrd and N. I. M. Gould and J. Nocedal and R. A.
		  Waltz},
  date-modified	= {2007-10-22 16:57:18 -0700},
  journal	= mathprog,
  number	= 1,
  pages		= {27--48},
  title		= {An algorithm for nonlinear optimization using linear
		  programming and equality constrained subproblems},
  volume	= 100,
  year		= 2004
}

###Article{	  byrdgoulnocewalt:2004,
  author	= {R. H. Byrd and N. I. M. Gould and J. Nocedal and R. A.
		  Waltz},
  date-modified	= {2007-10-22 16:57:18 -0700},
  journal	= mathprog,
  number	= 1,
  pages		= {27--48},
  title		= {An algorithm for nonlinear optimization using linear
		  programming and equality constrained subproblems},
  volume	= 100,
  year		= 2004
}

@TechReport{	  byrdgoulnocewalt:2004b,
  address	= {Northwestern University, Evanston, IL},
  author	= {R. H. Byrd and N. I. M. Gould and J. Nocedal and R. A.
		  Waltz},
  date-modified	= {2007-10-19 14:47:06 -0700},
  institution	= {Optimization Technology Center},
  number	= {OTC 5/2002},
  title		= {On the convergence of successive linear programming
		  algorithms},
  type		= {Tech. rep.},
  year		= 2004
}

###TechReport{	  byrdgoulnocewalt:2004b,
  address	= {Northwestern University, Evanston, IL},
  author	= {R. H. Byrd and N. I. M. Gould and J. Nocedal and R. A.
		  Waltz},
  date-modified	= {2007-10-19 14:47:06 -0700},
  institution	= {Optimization Technology Center},
  number	= {OTC 5/2002},
  title		= {On the convergence of successive linear programming
		  algorithms},
  type		= {Tech. rep.},
  year		= 2004
}

###TechReport{	  byrdgoulnocewalt:2004b,
  address	= {Northwestern University, Evanston, IL},
  author	= {R. H. Byrd and N. I. M. Gould and J. Nocedal and R. A.
		  Waltz},
  date-modified	= {2007-10-19 14:47:06 -0700},
  institution	= {Optimization Technology Center},
  number	= {OTC 5/2002},
  title		= {On the convergence of successive linear programming
		  algorithms},
  type		= {Tech. rep.},
  year		= 2004
}

@Article{	  byrdhribnoce:1999,
  author	= {R. Byrd and M. E. Hribar and J. Nocedal},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {877-900},
  title		= {An Interior Point Method for Large Scale Nonlinear
		  Programming},
  volume	= 9,
  year		= 1999
}

###Article{	  byrdhribnoce:1999,
  author	= {R. Byrd and M. E. Hribar and J. Nocedal},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {877-900},
  title		= {An Interior Point Method for Large Scale Nonlinear
		  Programming},
  volume	= 9,
  year		= 1999
}

###Article{	  byrdhribnoce:1999,
  author	= {R. Byrd and M. E. Hribar and J. Nocedal},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {877-900},
  title		= {An Interior Point Method for Large Scale Nonlinear
		  Programming},
  volume	= 9,
  year		= 1999
}

@InCollection{	  byrdnocewalt:06,
  author	= {R. H. Byrd and J. Nocedal and R. A. Waltz},
  title		= {{KNITRO}: An Integrated Package for Nonlinear
		  Optimization},
  booktitle	= {Large-Scale Nonlinear Optimization},
  pages		= {35-59},
  publisher	= {Springer-Verlag},
  address	= {New York},
  year		= 2006,
  editor	= {G. di Pillo and M. Roma}
}

###InCollection{  byrdnocewalt:06,
  author	= {R. H. Byrd and J. Nocedal and R. A. Waltz},
  title		= {{KNITRO}: An Integrated Package for Nonlinear
		  Optimization},
  booktitle	= {Large-Scale Nonlinear Optimization},
  pages		= {35-59},
  publisher	= {Springer-Verlag},
  address	= {New York},
  year		= 2006,
  editor	= {G. di Pillo and M. Roma}
}

###InCollection{  byrdnocewalt:06,
  author	= {R. H. Byrd and J. Nocedal and R. A. Waltz},
  title		= {{KNITRO}: An Integrated Package for Nonlinear
		  Optimization},
  booktitle	= {Large-Scale Nonlinear Optimization},
  pages		= {35-59},
  publisher	= {Springer-Verlag},
  address	= {New York},
  year		= 2006,
  editor	= {G. di Pillo and M. Roma}
}

@Article{	  byrdschnshul:1987,
  author	= {R. H. Byrd and R. B. Schnabel and G. A. Shultz},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamnumanal,
  month		= {October},
  number	= 5,
  pages		= {1152-1170},
  title		= {A Trust Region Algorithm for Nonlinearly Constrained
		  Optimization},
  volume	= 24,
  year		= 1987
}

###Article{	  byrdschnshul:1987,
  author	= {R. H. Byrd and R. B. Schnabel and G. A. Shultz},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamnumanal,
  month		= {October},
  number	= 5,
  pages		= {1152-1170},
  title		= {A Trust Region Algorithm for Nonlinearly Constrained
		  Optimization},
  volume	= 24,
  year		= 1987
}

###Article{	  byrdschnshul:1987,
  author	= {R. H. Byrd and R. B. Schnabel and G. A. Shultz},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamnumanal,
  month		= {October},
  number	= 5,
  pages		= {1152-1170},
  title		= {A Trust Region Algorithm for Nonlinearly Constrained
		  Optimization},
  volume	= 24,
  year		= 1987
}

@Article{	  calvlewireicsgal:2004,
  abstract	= {Many numerical methods for the solution of ill-posed
		  problems are based on Tikhonov regularization. Recently,
		  Rojas and Steihaug described a barrier method for computing
		  nonnegative Tikhonov-regularized approximate solutions of
		  linear discrete ill-posed problems. Their method is based
		  on solving a sequence of parameterized eigenvalue problems.
		  This paper describes how the solution of parametrized
		  eigenvalue problems can be avoided by computing bounds that
		  follow from the connection between the Lanczos process,
		  orthogonal polynomials and Gauss quadrature. },
  author	= {Daniela Calvetti and Bryan Lewis and Lothar Reichel and
		  Fiorella Sgallari},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Electronic Transactions on Numerical Analysis},
  keywords	= {Ill-posed problem, inverse problem, solution constraint,
		  Lanczos methods, Gauss quadrature},
  pages		= {153--173},
  title		= {Tikhonov regularization with nonnegativity constraint},
  volume	= 18,
  year		= 2004
}

###Article{	  calvlewireicsgal:2004,
  abstract	= {Many numerical methods for the solution of ill-posed
		  problems are based on Tikhonov regularization. Recently,
		  Rojas and Steihaug described a barrier method for computing
		  nonnegative Tikhonov-regularized approximate solutions of
		  linear discrete ill-posed problems. Their method is based
		  on solving a sequence of parameterized eigenvalue problems.
		  This paper describes how the solution of parametrized
		  eigenvalue problems can be avoided by computing bounds that
		  follow from the connection between the Lanczos process,
		  orthogonal polynomials and Gauss quadrature. },
  author	= {Daniela Calvetti and Bryan Lewis and Lothar Reichel and
		  Fiorella Sgallari},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Electronic Transactions on Numerical Analysis},
  keywords	= {Ill-posed problem, inverse problem, solution constraint,
		  Lanczos methods, Gauss quadrature},
  pages		= {153--173},
  title		= {Tikhonov regularization with nonnegativity constraint},
  volume	= 18,
  year		= 2004
}

###Article{	  calvlewireicsgal:2004,
  abstract	= {Many numerical methods for the solution of ill-posed
		  problems are based on Tikhonov regularization. Recently,
		  Rojas and Steihaug described a barrier method for computing
		  nonnegative Tikhonov-regularized approximate solutions of
		  linear discrete ill-posed problems. Their method is based
		  on solving a sequence of parameterized eigenvalue problems.
		  This paper describes how the solution of parametrized
		  eigenvalue problems can be avoided by computing bounds that
		  follow from the connection between the Lanczos process,
		  orthogonal polynomials and Gauss quadrature. },
  author	= {Daniela Calvetti and Bryan Lewis and Lothar Reichel and
		  Fiorella Sgallari},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Electronic Transactions on Numerical Analysis},
  keywords	= {Ill-posed problem, inverse problem, solution constraint,
		  Lanczos methods, Gauss quadrature},
  pages		= {153--173},
  title		= {Tikhonov regularization with nonnegativity constraint},
  volume	= 18,
  year		= 2004
}

@Article{	  can08,
  title		= {The restricted isometry property and its implications for
		  compressed sensing},
  author	= {Cand\`es, E.J.},
  journal	= {Comptes rendus-Math{\'e}matique},
  volume	= 346,
  number	= {9-10},
  pages		= {589--592},
  year		= 2008
}

###Article{	  can08,
  title		= {The restricted isometry property and its implications for
		  compressed sensing},
  author	= {Cand\`es, E.J.},
  journal	= {Comptes rendus-Math{\'e}matique},
  volume	= 346,
  number	= {9-10},
  pages		= {589--592},
  year		= 2008
}

###Article{	  can08,
  title		= {The restricted isometry property and its implications for
		  compressed sensing},
  author	= {Cand\`es, E.J.},
  journal	= {Comptes rendus-Math{\'e}matique},
  volume	= 346,
  number	= {9-10},
  pages		= {589--592},
  year		= 2008
}

@InProceedings{	  cand:2006,
  author	= {E. J. Cand\`{e}s},
  booktitle	= {Proceedings of the International Congress of
		  Mathematicians},
  location	= {Madrid, Spain},
  title		= {Compressive sampling},
  year		= 2006
}

###InProceedings{ cand:2006,
  author	= {E. J. Cand\`{e}s},
  booktitle	= {Proceedings of the International Congress of
		  Mathematicians},
  location	= {Madrid, Spain},
  title		= {Compressive sampling},
  year		= 2006
}

###InProceedings{ cand:2006,
  author	= {E. J. Cand\`{e}s},
  booktitle	= {Proceedings of the International Congress of
		  Mathematicians},
  location	= {Madrid, Spain},
  title		= {Compressive sampling},
  year		= 2006
}

@Article{	  canddemadonoying:2005,
  author	= {E. J. Cand\`{e}s and L. Demanet and D. L. Donoho and L.-X.
		  Ying},
  title		= {Fast discrete curvelet transforms},
  journal	= siammms,
  volume	= 5,
  pages		= {861--899},
  year		= 2005
}

###Article{	  canddemadonoying:2005,
  author	= {E. J. Cand\`{e}s and L. Demanet and D. L. Donoho and L.-X.
		  Ying},
  title		= {Fast discrete curvelet transforms},
  journal	= siammms,
  volume	= 5,
  pages		= {861--899},
  year		= 2005
}

###Article{	  canddemadonoying:2005,
  author	= {E. J. Cand\`{e}s and L. Demanet and D. L. Donoho and L.-X.
		  Ying},
  title		= {Fast discrete curvelet transforms},
  journal	= siammms,
  volume	= 5,
  pages		= {861--899},
  year		= 2005
}

@TechReport{	  candes2008,
  author	= "Candes, E. J. and Recht, B.",
  title		= "Exact Matrix Completion via Convex Optimization",
  institution	= "California Institute of Technology",
  address	= "Pasadena",
  month		= "May",
  year		= 2008
}

###TechReport{	  candes2008,
  author	= "Candes, E. J. and Recht, B.",
  title		= "Exact Matrix Completion via Convex Optimization",
  institution	= "California Institute of Technology",
  address	= "Pasadena",
  month		= "May",
  year		= 2008
}

###TechReport{	  candes2008,
  author	= "Candes, E. J. and Recht, B.",
  title		= "Exact Matrix Completion via Convex Optimization",
  institution	= "California Institute of Technology",
  address	= "Pasadena",
  month		= "May",
  year		= 2008
}

@Article{	  candes2012phaselift,
  title		= {PhaseLift: Exact and stable signal recovery from magnitude
		  measurements via convex programming},
  author	= {Cand\`es, Emmanuel J and Strohmer, Thomas and Voroninski,
		  Vladislav},
  journal	= {Commun. Pur. Appl. Ana.},
  year		= 2012,
  publisher	= {Wiley Online Library}
}

###Article{	  candes2012phaselift,
  title		= {PhaseLift: Exact and stable signal recovery from magnitude
		  measurements via convex programming},
  author	= {Cand\`es, Emmanuel J and Strohmer, Thomas and Voroninski,
		  Vladislav},
  journal	= {Commun. Pur. Appl. Ana.},
  year		= 2012,
  publisher	= {Wiley Online Library}
}

@Misc{		  candeslisoltanolkotabi:2014,
  author	= {Emmanuel Cand\`es and Xiaodong Li and Mahdi
		  Soltanolkotabi},
  title		= {Phase Retrieval via Wirtinger Flow: Theory and
		  Algorithms},
  year		= 2014,
  eprint	= {arXiv:1407.1065},
  url		= {http://arxiv.org/abs/1407.1065}
}

###Misc{	  candeslisoltanolkotabi:2014,
  author	= {Emmanuel Cand\`es and Xiaodong Li and Mahdi
		  Soltanolkotabi},
  title		= {Phase Retrieval via Wirtinger Flow: Theory and
		  Algorithms},
  year		= 2014,
  eprint	= {arXiv:1407.1065},
  url		= {http://arxiv.org/abs/1407.1065}
}

@InProceedings{	  candesromberg:2006,
  author	= {E. J. Cand\`{e}s and J. Romberg},
  booktitle	= {Computational Imaging III, Proc. SPIE Conf.},
  title		= {Practical Signal Recovery from Random Projections},
  editors	= {Charles A. Bouman, Eric L. Miller},
  volume	= 5914,
  pages		= {76--86},
  month		= {March},
  year		= 2005
}

###InProceedings{ candesromberg:2006,
  author	= {E. J. Cand\`{e}s and J. Romberg},
  booktitle	= {Computational Imaging III, Proc. SPIE Conf.},
  title		= {Practical Signal Recovery from Random Projections},
  editors	= {Charles A. Bouman, Eric L. Miller},
  volume	= 5914,
  pages		= {76--86},
  month		= {March},
  year		= 2005
}

###InProceedings{ candesromberg:2006,
  author	= {E. J. Cand\`{e}s and J. Romberg},
  booktitle	= {Computational Imaging III, Proc. SPIE Conf.},
  title		= {Practical Signal Recovery from Random Projections},
  editors	= {Charles A. Bouman, Eric L. Miller},
  volume	= 5914,
  pages		= {76--86},
  month		= {March},
  year		= 2005
}

@Article{	  candestao:2010,
  author	= {Emmanuel J. Cand\`{e}s and Terence Tao},
  title		= {The power of convex relaxation: near-optimal matrix
		  completion},
  year		= 2010,
  journal	= ieeetransinfo,
  month		= {May},
  pages		= {2053-2080},
  doi		= {10.1109/TIT.2010.2044061},
  volume	= 56
}

###Article{	  candestao:2010,
  author	= {Emmanuel J. Cand\`{e}s and Terence Tao},
  title		= {The power of convex relaxation: near-optimal matrix
		  completion},
  year		= 2010,
  journal	= ieeetransinfo,
  month		= {May},
  pages		= {2053-2080},
  doi		= {10.1109/TIT.2010.2044061},
  volume	= 56
}

###Article{	  candestao:2010,
  author	= {Emmanuel J. Cand\`{e}s and Terence Tao},
  title		= {The power of convex relaxation: near-optimal matrix
		  completion},
  year		= 2010,
  journal	= ieeetransinfo,
  month		= {May},
  pages		= {2053-2080},
  doi		= {10.1109/TIT.2010.2044061},
  volume	= 56
}

@Article{	  candeswakinboyd:2008,
  author	= {E. J. Cand\'es and M. B. Wakin and S. P. Boyd},
  title		= {Enhancing sparsity by reweighted {L1} minimization},
  journal	= {J. Fourier Anal. Appl.},
  year		= 2008,
  volume	= 14,
  number	= 5,
  pages		= {877--905},
  doi		= {10.1007/s00041-008-9045-x}
}

###Article{	  candeswakinboyd:2008,
  author	= {E. J. Cand\'es and M. B. Wakin and S. P. Boyd},
  title		= {Enhancing sparsity by reweighted {L1} minimization},
  journal	= {J. Fourier Anal. Appl.},
  year		= 2008,
  volume	= 14,
  number	= 5,
  pages		= {877--905},
  doi		= {10.1007/s00041-008-9045-x}
}

@Article{	  candplan:2010,
  author	= {Emmanuel J. Cand\`{e}s and Yaniv Plan},
  title		= {Matrix completion with noise},
  journal	= {Proc. IEEE},
  year		= 2010,
  pages		= {925-936},
  volume	= 98,
  doi		= {10.1109/JPROC.2009.2035722}
}

###Article{	  candplan:2010,
  author	= {Emmanuel J. Cand\`{e}s and Yaniv Plan},
  title		= {Matrix completion with noise},
  journal	= {Proc. IEEE},
  year		= 2010,
  pages		= {925-936},
  volume	= 98,
  doi		= {10.1109/JPROC.2009.2035722}
}

###Article{	  candplan:2010,
  author	= {Emmanuel J. Cand\`{e}s and Yaniv Plan},
  title		= {Matrix completion with noise},
  journal	= {Proc. IEEE},
  year		= 2010,
  pages		= {925-936},
  volume	= 98,
  doi		= {10.1109/JPROC.2009.2035722}
}

@Misc{		  candromb:2007,
  author	= {E. J. Cand\`{e}s and J. Romberg},
  date-modified	= {2007-07-26 16:37:57 -0700},
  howpublished	= {\url{http://www.l1-magic.org/}},
  title		= {{$\ell_1$-magic}},
  year		= 2007
}

###Misc{	  candromb:2007,
  author	= {E. J. Cand\`{e}s and J. Romberg},
  date-modified	= {2007-07-26 16:37:57 -0700},
  howpublished	= {\url{http://www.l1-magic.org/}},
  title		= {{$\ell_1$-magic}},
  year		= 2007
}

###Misc{	  candromb:2007,
  author	= {E. J. Cand\`{e}s and J. Romberg},
  date-modified	= {2007-07-26 16:37:57 -0700},
  howpublished	= {\url{http://www.l1-magic.org/}},
  title		= {{$\ell_1$-magic}},
  year		= 2007
}

@Article{	  candtao:2006,
  author	= {E. J. Cand\`{e}s and T. Tao},
  doi		= {10.1109/TIT.2006.885507},
  journal	= ieeetransinfo,
  month		= {December},
  number	= 12,
  pages		= {5406--5425},
  title		= {Near-optimal signal recovery from random projections:
		  Universal encoding strategies?},
  volume	= 52,
  year		= 2006
}

###Article{	  candtao:2006,
  author	= {E. J. Cand\`{e}s and T. Tao},
  doi		= {10.1109/TIT.2006.885507},
  journal	= ieeetransinfo,
  month		= {December},
  number	= 12,
  pages		= {5406--5425},
  title		= {Near-optimal signal recovery from random projections:
		  Universal encoding strategies?},
  volume	= 52,
  year		= 2006
}

###Article{	  candtao:2006,
  author	= {E. J. Cand\`{e}s and T. Tao},
  doi		= {10.1109/TIT.2006.885507},
  journal	= ieeetransinfo,
  month		= {December},
  number	= 12,
  pages		= {5406--5425},
  title		= {Near-optimal signal recovery from random projections:
		  Universal encoding strategies?},
  volume	= 52,
  year		= 2006
}

@Misc{		  candtao:2009,
  author	= {Emmanuel J. Cand\`{e}s and Terence Tao},
  title		= {The power of convex relaxation: near-optimal matrix
		  completion},
  url		= {http://www.acm.caltech.edu/~emmanuel/papers/NoisyCompletion.pdf},
  howpublished	= {Submitted},
  month		= {March},
  year		= 2009
}

###Article{	  candtao:2009,
  title		= {The power of convex relaxation: Near-optimal matrix
		  completion},
  author	= {Cand{\`e}s, Emmanuel J and Tao, Terence},
  journal	= ieeetransinfo,
  volume	= 56,
  number	= 5,
  pages		= {2053--2080},
  year		= 2010,
  publisher	= {IEEE}
}

###Article{	  candtao:2009,
  title		= {The power of convex relaxation: Near-optimal matrix
		  completion},
  author	= {Cand{\`e}s, Emmanuel J and Tao, Terence},
  journal	= ieeetransinfo,
  volume	= 56,
  number	= 5,
  pages		= {2053--2080},
  year		= 2010,
  publisher	= {IEEE}
}

@TechReport{	  candwakiboyd:2007tr,
  author	= {Cand\'es, E. J. and Wakin, M. B. and Boyd, S. P.},
  title		= {Enhancing Sparsity by Reweighted {L1} Minimization},
  institution	= {California Institute of Technology},
  year		= 2007,
  month		= {October},
  note		= {Available at
		  \url{http://www.acm.caltech.edu/~emmanuel/papers/rwl1-oct2007.pdf}}
}

###TechReport{	  candwakiboyd:2007tr,
  author	= {Cand\'es, E. J. and Wakin, M. B. and Boyd, S. P.},
  title		= {Enhancing Sparsity by Reweighted {L1} Minimization},
  institution	= {California Institute of Technology},
  year		= 2007,
  month		= {October},
  note		= {Available at
		  \url{http://www.acm.caltech.edu/~emmanuel/papers/rwl1-oct2007.pdf}}
}

###TechReport{	  candwakiboyd:2007tr,
  author	= {Cand\'es, E. J. and Wakin, M. B. and Boyd, S. P.},
  title		= {Enhancing Sparsity by Reweighted {L1} Minimization},
  institution	= {California Institute of Technology},
  year		= 2007,
  month		= {October},
  note		= {Available at
		  \url{http://www.acm.caltech.edu/~emmanuel/papers/rwl1-oct2007.pdf}}
}

@Article{	  candwakiboyd:2008,
  author	= {E. J. Cand\'es and M. B. Wakin and S. P. Boyd},
  title		= {Enhancing sparsity by reweighted {L1} minimization},
  journal	= {J. Fourier Anal. Appl.},
  year		= 2008,
  volume	= 14,
  number	= 5,
  pages		= {877--905},
  doi		= {10.1007/s00041-008-9045-x}
}

@Article{	  canr:2009,
  author	= "Emmanuel J. Candes and Ben Recht",
  title		= "Exact Matrix Completion via Convex Optimization",
  volume	= 9,
  year		= 2009,
  pages		= {717-772}
}

###Article{	  canr:2009,
  author	= "Emmanuel J. Candes and Ben Recht",
  title		= "Exact Matrix Completion via Convex Optimization",
  volume	= 9,
  month		= {December},
  journal	= focm,
  year		= 2009,
  pages		= {717-772}
}

###Article{	  canr:2009,
  author	= "Emmanuel J. Candes and Ben Recht",
  title		= "Exact Matrix Completion via Convex Optimization",
  volume	= 9,
  month		= {December},
  journal	= focm,
  year		= 2009,
  pages		= {717-772}
}

@Misc{		  car08,
  author	= {Emmanuel J. Cand\`{e}s and Benjamin Recht},
  title		= {Exact matrix completion via convex optimization},
  url		= {http://www.acm.caltech.edu/~emmanuel/papers/NoisyCompletion.pdf},
  howpublished	= {To appear in Found. of Comput. Math.},
  year		= 2008
}

@Article{	  cat05,
  abstract	= {This paper considers a natural error correcting problem
		  with real valued input/output. We wish to recover an input
		  vector f/spl isin/R/sup n/ from corrupted measurements
		  y=Af+e. Here, A is an m by n (coding) matrix and e is an
		  arbitrary and unknown vector of errors. Is it possible to
		  recover f exactly from the data y? We prove that under
		  suitable conditions on the coding matrix A, the input f is
		  the unique solution to the /spl lscr//sub 1/-minimization
		  problem (/spl par/x/spl par//sub /spl lscr/1/:=/spl
		  Sigma//sub i/|x/sub i/|) min(g/spl isin/R/sup n/) /spl
		  par/y - Ag/spl par//sub /spl lscr/1/ provided that the
		  support of the vector of errors is not too large, /spl
		  par/e/spl par//sub /spl lscr/0/:=|{i:e/sub i/ /spl ne/
		  0}|/spl les//spl rho//spl middot/m for some /spl rho/>0. In
		  short, f can be recovered exactly by solving a simple
		  convex optimization problem (which one can recast as a
		  linear program). In addition, numerical experiments suggest
		  that this recovery procedure works unreasonably well; f is
		  recovered exactly even in situations where a significant
		  fraction of the output is corrupted. This work is related
		  to the problem of finding sparse solutions to vastly
		  underdetermined systems of linear equations. There are also
		  significant connections with the problem of recovering
		  signals from highly incomplete measurements. In fact, the
		  results introduced in this paper improve on our earlier
		  work. Finally, underlying the success of /spl lscr//sub 1/
		  is a crucial property we call the uniform uncertainty
		  principle that we shall describe in detail.},
  author	= {E. J. Cand\`{e}s and T. Tao},
  journal	= ieeetransinfo,
  month		= {December},
  number	= 12,
  pages		= {4203-4215},
  title		= {Decoding by linear programming},
  volume	= 51,
  year		= 2005
}

###Article{	  cat05,
  abstract	= {This paper considers a natural error correcting problem
		  with real valued input/output. We wish to recover an input
		  vector f/spl isin/R/sup n/ from corrupted measurements
		  y=Af+e. Here, A is an m by n (coding) matrix and e is an
		  arbitrary and unknown vector of errors. Is it possible to
		  recover f exactly from the data y? We prove that under
		  suitable conditions on the coding matrix A, the input f is
		  the unique solution to the /spl lscr//sub 1/-minimization
		  problem (/spl par/x/spl par//sub /spl lscr/1/:=/spl
		  Sigma//sub i/|x/sub i/|) min(g/spl isin/R/sup n/) /spl
		  par/y - Ag/spl par//sub /spl lscr/1/ provided that the
		  support of the vector of errors is not too large, /spl
		  par/e/spl par//sub /spl lscr/0/:=|{i:e/sub i/ /spl ne/
		  0}|/spl les//spl rho//spl middot/m for some /spl rho/>0. In
		  short, f can be recovered exactly by solving a simple
		  convex optimization problem (which one can recast as a
		  linear program). In addition, numerical experiments suggest
		  that this recovery procedure works unreasonably well; f is
		  recovered exactly even in situations where a significant
		  fraction of the output is corrupted. This work is related
		  to the problem of finding sparse solutions to vastly
		  underdetermined systems of linear equations. There are also
		  significant connections with the problem of recovering
		  signals from highly incomplete measurements. In fact, the
		  results introduced in this paper improve on our earlier
		  work. Finally, underlying the success of /spl lscr//sub 1/
		  is a crucial property we call the uniform uncertainty
		  principle that we shall describe in detail.},
  author	= {E. J. Cand\`{e}s and T. Tao},
  journal	= ieeetransinfo,
  month		= {December},
  number	= 12,
  pages		= {4203-4215},
  title		= {Decoding by linear programming},
  volume	= 51,
  year		= 2005
}

###Article{	  cat05,
  abstract	= {This paper considers a natural error correcting problem
		  with real valued input/output. We wish to recover an input
		  vector f/spl isin/R/sup n/ from corrupted measurements
		  y=Af+e. Here, A is an m by n (coding) matrix and e is an
		  arbitrary and unknown vector of errors. Is it possible to
		  recover f exactly from the data y? We prove that under
		  suitable conditions on the coding matrix A, the input f is
		  the unique solution to the /spl lscr//sub 1/-minimization
		  problem (/spl par/x/spl par//sub /spl lscr/1/:=/spl
		  Sigma//sub i/|x/sub i/|) min(g/spl isin/R/sup n/) /spl
		  par/y - Ag/spl par//sub /spl lscr/1/ provided that the
		  support of the vector of errors is not too large, /spl
		  par/e/spl par//sub /spl lscr/0/:=|{i:e/sub i/ /spl ne/
		  0}|/spl les//spl rho//spl middot/m for some /spl rho/>0. In
		  short, f can be recovered exactly by solving a simple
		  convex optimization problem (which one can recast as a
		  linear program). In addition, numerical experiments suggest
		  that this recovery procedure works unreasonably well; f is
		  recovered exactly even in situations where a significant
		  fraction of the output is corrupted. This work is related
		  to the problem of finding sparse solutions to vastly
		  underdetermined systems of linear equations. There are also
		  significant connections with the problem of recovering
		  signals from highly incomplete measurements. In fact, the
		  results introduced in this paper improve on our earlier
		  work. Finally, underlying the success of /spl lscr//sub 1/
		  is a crucial property we call the uniform uncertainty
		  principle that we shall describe in detail.},
  author	= {E. J. Cand\`{e}s and T. Tao},
  journal	= ieeetransinfo,
  month		= {December},
  number	= 12,
  pages		= {4203-4215},
  title		= {Decoding by linear programming},
  volume	= 51,
  year		= 2005
}

@TechReport{	  ccs08,
  author	= "Jian-Feng Cai and Emmanuel J. Cand\'es and Zuowei Shen",
  title		= "A singular value thresholding algorithm for matrix
		  completion",
  type		= "Report",
  institution	= "California Institute of Technology",
  address	= "Pasadena",
  month		= "September",
  url		= {http://www.acm.caltech.edu/~emmanuel/papers/SVT.pdf},
  year		= 2008
}

###TechReport{	  ccs08,
  author	= "Jian-Feng Cai and Emmanuel J. Cand\'es and Zuowei Shen",
  title		= "A singular value thresholding algorithm for matrix
		  completion",
  type		= "Report",
  institution	= "California Institute of Technology",
  address	= "Pasadena",
  month		= "September",
  url		= {http://www.acm.caltech.edu/~emmanuel/papers/SVT.pdf},
  year		= 2008
}

###TechReport{	  ccs08,
  author	= "Jian-Feng Cai and Emmanuel J. Cand\'es and Zuowei Shen",
  title		= "A singular value thresholding algorithm for matrix
		  completion",
  type		= "Report",
  institution	= "California Institute of Technology",
  address	= "Pasadena",
  month		= "September",
  url		= {http://www.acm.caltech.edu/~emmanuel/papers/SVT.pdf},
  year		= 2008
}

@Article{	  ccs10,
  author	= "Jian-Feng Cai and Emmanuel J. Cand\'es and Zuowei Shen",
  title		= "A Singular Value Thresholding Algorithm for Matrix
		  Completion",
  journal	= siamopt,
  number	= 20,
  pages		= {1956-1982},
  doi		= {10.1137/080738970},
  year		= 2010
}

###Article{	  ccs10,
  author	= "Jian-Feng Cai and Emmanuel J. Cand\'es and Zuowei Shen",
  title		= "A Singular Value Thresholding Algorithm for Matrix
		  Completion",
  journal	= siamopt,
  number	= 20,
  volume	= 4,
  pages		= {1956-1982},
  doi		= {10.1137/080738970},
  year		= 2010
}

###Article{	  ccs10,
  author	= "Jian-Feng Cai and Emmanuel J. Cand\'es and Zuowei Shen",
  title		= "A Singular Value Thresholding Algorithm for Matrix
		  Completion",
  journal	= siamopt,
  number	= 20,
  volume	= 4,
  pages		= {1956-1982},
  doi		= {10.1137/080738970},
  year		= 2010
}

@Article{	  cds98,
  author	= {S. S. Chen and D. L. Donoho and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamscicomp,
  number	= 1,
  pages		= {33-61},
  publisher	= {SIAM},
  title		= {Atomic decomposition by basis pursuit},
  volume	= 20,
  year		= 1998
}

###Article{	  cds98,
  author	= {S. S. Chen and D. L. Donoho and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamscicomp,
  number	= 1,
  pages		= {33-61},
  publisher	= {SIAM},
  title		= {Atomic decomposition by basis pursuit},
  volume	= 20,
  year		= 1998
}

###Article{	  cds98,
  author	= {S. S. Chen and D. L. Donoho and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamscicomp,
  number	= 1,
  pages		= {33-61},
  publisher	= {SIAM},
  title		= {Atomic decomposition by basis pursuit},
  volume	= 20,
  year		= 1998
}

@Book{		  cez97,
  author	= "Censor, Y. and Zenios, S.",
  title		= "Parallel Optimization: Theory, Algoritluns, and
		  Applications",
  publisher	= "Oxford University Press",
  year		= 1997,
  address	= "New York"
}

###Book{	  cez97,
  author	= "Censor, Y. and Zenios, S.",
  title		= "Parallel Optimization: Theory, Algoritluns, and
		  Applications",
  publisher	= "Oxford University Press",
  year		= 1997,
  address	= "New York"
}

###Book{	  cez97,
  author	= "Censor, Y. and Zenios, S.",
  title		= "Parallel Optimization: Theory, Algoritluns, and
		  Applications",
  publisher	= "Oxford University Press",
  year		= 1997,
  address	= "New York"
}

@Article{	  chaimoscosopapanicolaou:2011,
  author	= {Anwei Chai and Miguel Moscoso and George Papanicolaou},
  title		= {Array imaging using intensity-only measurements},
  journal	= {Inverse Problems},
  volume	= 27,
  number	= 1,
  pages		= 015005,
  url		= {http://stacks.iop.org/0266-5611/27/i=1/a=015005},
  year		= 2011,
  abstract	= {We introduce a new approach for narrow band array imaging
		  of localized scatterers from intensity-only measurements by
		  considering the possibility of reconstructing the positions
		  and reflectivities of the scatterers exactly from only
		  partial knowledge of the array data, since we assume that
		  phase information is not available. We reformulate this
		  intensity-only imaging problem as a non-convex optimization
		  problem and show that we can have exact recovery by
		  minimizing the rank of a positive semidefinite matrix
		  associated with the unknown reflectivities. Since this
		  optimization problem is NP-hard and is computationally
		  intractable, we replace the rank of the matrix by its
		  nuclear norm, the sum of its singular values, which is a
		  convex programming problem that can be solved in polynomial
		  time. Numerical experiments explore the robustness of this
		  approach, which recovers sparse reflectivity vectors
		  exactly as solutions of a convex optimization problem.}
}

###Article{	  chaimoscosopapanicolaou:2011,
  author	= {Anwei Chai and Miguel Moscoso and George Papanicolaou},
  title		= {Array imaging using intensity-only measurements},
  journal	= {Inverse Problems},
  volume	= 27,
  number	= 1,
  pages		= 015005,
  url		= {http://stacks.iop.org/0266-5611/27/i=1/a=015005},
  year		= 2011,
  abstract	= {We introduce a new approach for narrow band array imaging
		  of localized scatterers from intensity-only measurements by
		  considering the possibility of reconstructing the positions
		  and reflectivities of the scatterers exactly from only
		  partial knowledge of the array data, since we assume that
		  phase information is not available. We reformulate this
		  intensity-only imaging problem as a non-convex optimization
		  problem and show that we can have exact recovery by
		  minimizing the rank of a positive semidefinite matrix
		  associated with the unknown reflectivities. Since this
		  optimization problem is NP-hard and is computationally
		  intractable, we replace the rank of the matrix by its
		  nuclear norm, the sum of its singular values, which is a
		  convex programming problem that can be solved in polynomial
		  time. Numerical experiments explore the robustness of this
		  approach, which recovers sparse reflectivity vectors
		  exactly as solutions of a convex optimization problem.}
}

@Article{	  chamdevoleeluci:1998,
  author	= {Chambolle, A. and De Vore, R.A. and Nam-Yong Lee and
		  Lucier, B.J.},
  date-added	= {2008-01-06 20:42:42 -0800},
  date-modified	= {2008-01-08 20:52:36 -0800},
  doi		= {10.1109/83.661182},
  issn		= {1057-7149},
  journal	= ieeetransimagproc,
  keywords	= {Gaussian noise, data compression, image coding, image
		  representation, minimisation, parameter estimation,
		  transform coding, wavelet transformsGaussian noise, SNR,
		  SureShrink method, accurate error bounds, approximate
		  minimizers, coding error, dyadic level, exact minimizers,
		  experimental results, i.i.d. noise, image compression, mean
		  zero noise, near-optimal shrinkage parameters, noise
		  removal, nonlinear wavelet image processing, parameter
		  estimation, shrinkage parameters, signal-to-noise ratio,
		  variational problems, visual perception, wavelet based
		  image processing algorithms, wavelet representation,
		  wavelet shrinkage},
  month		= {March},
  number	= 3,
  pages		= {319-335},
  title		= {Nonlinear wavelet image processing: variational problems,
		  compression, and noise removal through wavelet shrinkage},
  volume	= 7,
  year		= 1998
}

###Article{	  chamdevoleeluci:1998,
  author	= {Chambolle, A. and De Vore, R.A. and Nam-Yong Lee and
		  Lucier, B.J.},
  date-added	= {2008-01-06 20:42:42 -0800},
  date-modified	= {2008-01-08 20:52:36 -0800},
  doi		= {10.1109/83.661182},
  journal	= ieeetransimproc,
  keywords	= {Gaussian noise, data compression, image coding, image
		  representation, minimisation, parameter estimation,
		  transform coding, wavelet transformsGaussian noise, SNR,
		  SureShrink method, accurate error bounds, approximate
		  minimizers, coding error, dyadic level, exact minimizers,
		  experimental results, i.i.d. noise, image compression, mean
		  zero noise, near-optimal shrinkage parameters, noise
		  removal, nonlinear wavelet image processing, parameter
		  estimation, shrinkage parameters, signal-to-noise ratio,
		  variational problems, visual perception, wavelet based
		  image processing algorithms, wavelet representation,
		  wavelet shrinkage},
  month		= {March},
  number	= 3,
  pages		= {319-335},
  title		= {Nonlinear wavelet image processing: variational problems,
		  compression, and noise removal through wavelet shrinkage},
  volume	= 7,
  year		= 1998
}

###Article{	  chamdevoleeluci:1998,
  author	= {Chambolle, A. and De Vore, R.A. and Nam-Yong Lee and
		  Lucier, B.J.},
  date-added	= {2008-01-06 20:42:42 -0800},
  date-modified	= {2008-01-08 20:52:36 -0800},
  doi		= {10.1109/83.661182},
  journal	= ieeetransimproc,
  keywords	= {Gaussian noise, data compression, image coding, image
		  representation, minimisation, parameter estimation,
		  transform coding, wavelet transformsGaussian noise, SNR,
		  SureShrink method, accurate error bounds, approximate
		  minimizers, coding error, dyadic level, exact minimizers,
		  experimental results, i.i.d. noise, image compression, mean
		  zero noise, near-optimal shrinkage parameters, noise
		  removal, nonlinear wavelet image processing, parameter
		  estimation, shrinkage parameters, signal-to-noise ratio,
		  variational problems, visual perception, wavelet based
		  image processing algorithms, wavelet representation,
		  wavelet shrinkage},
  month		= {March},
  number	= 3,
  pages		= {319-335},
  title		= {Nonlinear wavelet image processing: variational problems,
		  compression, and noise removal through wavelet shrinkage},
  volume	= 7,
  year		= 1998
}

@Article{	  chan:2012,
  year		= 2012,
  journal	= {Found. Comput. Math.},
  volume	= 12,
  number	= 6,
  doi		= {10.1007/s10208-012-9135-7},
  title		= {The Convex Geometry of Linear Inverse Problems},
  publisher	= {Springer-Verlag},
  keywords	= {Convex optimization; Semidefinite programming; Atomic
		  norms; Real algebraic geometry; Gaussian width; Symmetry;
		  52A41; 90C25; 90C22; 60D05; 41A45},
  author	= {Chandrasekaran, Venkat and Recht, Benjamin and Parrilo,
		  PabloA. and Willsky, AlanS.},
  pages		= {805-849},
  language	= {English}
}

###Article{	  chan:2012,
  year		= 2012,
  journal	= {Found. Comput. Math.},
  volume	= 12,
  number	= 6,
  doi		= {10.1007/s10208-012-9135-7},
  title		= {The Convex Geometry of Linear Inverse Problems},
  publisher	= {Springer-Verlag},
  keywords	= {Convex optimization; Semidefinite programming; Atomic
		  norms; Real algebraic geometry; Gaussian width; Symmetry;
		  52A41; 90C25; 90C22; 60D05; 41A45},
  author	= {Chandrasekaran, Venkat and Recht, Benjamin and Parrilo,
		  PabloA. and Willsky, AlanS.},
  pages		= {805-849},
  language	= {English}
}

@Article{	  chanmorabaramitt:2008,
  author	= {W. L. Chan and M. L. Moravec and R. G. Baraniuk and D. M.
		  Mittleman},
  journal	= {Opt. Lett.},
  keywords	= {Ultrafast measurements; Terahertz imaging},
  number	= 9,
  pages		= {974--976},
  publisher	= {OSA},
  title		= {Terahertz imaging with compressed sensing and phase
		  retrieval},
  volume	= 33,
  year		= 2008,
  url		= {http://www.opticsinfobase.org/abstract.cfm?URI=ol-33-9-974}
}

###Article{	  chanmorabaramitt:2008,
  author	= {W. L. Chan and M. L. Moravec and R. G. Baraniuk and D. M.
		  Mittleman},
  journal	= {Opt. Lett.},
  keywords	= {Ultrafast measurements; Terahertz imaging},
  number	= 9,
  pages		= {974--976},
  publisher	= {OSA},
  title		= {Terahertz imaging with compressed sensing and phase
		  retrieval},
  volume	= 33,
  year		= 2008,
  url		= {http://www.opticsinfobase.org/abstract.cfm?URI=ol-33-9-974}
}

###Article{	  chanmorabaramitt:2008,
  author	= {W. L. Chan and M. L. Moravec and R. G. Baraniuk and D. M.
		  Mittleman},
  journal	= {Opt. Lett.},
  keywords	= {Ultrafast measurements; Terahertz imaging},
  number	= 9,
  pages		= {974--976},
  publisher	= {OSA},
  title		= {Terahertz imaging with compressed sensing and phase
		  retrieval},
  volume	= 33,
  year		= 2008,
  url		= {http://www.opticsinfobase.org/abstract.cfm?URI=ol-33-9-974}
}

@InProceedings{	  chartrandyin:2008,
  author	= {Chartrand, R. and Wotao Yin},
  booktitle	= {Acoustics, Speech and Signal Processing, 2008. ICASSP
		  2008. IEEE International Conference on},
  title		= {Iteratively reweighted algorithms for compressive
		  sensing},
  year		= 2008,
  month		= {31 2008-april 4},
  pages		= {3869 -3872},
  keywords	= {compressive sensing theory;iteratively reweighted
		  algorithm;reweighted least-squares algorithm;signal
		  reconstruction;iterative methods;least squares
		  approximations;signal reconstruction;},
  doi		= {10.1109/ICASSP.2008.4518498},
  issn		= {1520-6149}
}

###InProceedings{ chartrandyin:2008,
  author	= {Chartrand, R. and Wotao Yin},
  booktitle	= {Acoustics, Speech and Signal Processing, 2008. ICASSP
		  2008. IEEE International Conference on},
  title		= {Iteratively reweighted algorithms for compressive
		  sensing},
  year		= 2008,
  month		= {31 2008-april 4},
  pages		= {3869 -3872},
  keywords	= {compressive sensing theory;iteratively reweighted
		  algorithm;reweighted least-squares algorithm;signal
		  reconstruction;iterative methods;least squares
		  approximations;signal reconstruction;},
  doi		= {10.1109/ICASSP.2008.4518498}
}

###InProceedings{ chartrandyin:2008,
  author	= {Chartrand, R. and Wotao Yin},
  booktitle	= {Acoustics, Speech and Signal Processing, 2008. ICASSP
		  2008. IEEE International Conference on},
  title		= {Iteratively reweighted algorithms for compressive
		  sensing},
  year		= 2008,
  month		= {31 2008-april 4},
  pages		= {3869 -3872},
  keywords	= {compressive sensing theory;iteratively reweighted
		  algorithm;reweighted least-squares algorithm;signal
		  reconstruction;iterative methods;least squares
		  approximations;signal reconstruction;},
  doi		= {10.1109/ICASSP.2008.4518498}
}

@Article{	  chasanparwil11,
  author	= {V. Chandrasekaran and S. Sanghavi and P. P. Parrilo and A.
		  S. Willsky},
  title		= {Rank-Sparsity Incoherence for Matrix Decomposition},
  journal	= siamopt,
  year		= 2011,
  number	= 2,
  volume	= 21,
  pages		= {572-596},
  doi		= {10.1137/090761793},
  abstract	= {Suppose we are given a matrix that is formed by adding an
		  unknown sparse matrix to an unknown low-rank matrix. Our
		  goal is to decompose the given matrix into its sparse and
		  low-rank components. Such a problem arises in a number of
		  applications in model and system identification, and is
		  NP-hard in general. In this paper we consider a convex
		  optimization formulation to splitting the specified matrix
		  into its components, by minimizing a linear combination of
		  the $\ell_1$ norm and the nuclear norm of the components.
		  We develop a notion of rank-sparsity incoherence, expressed
		  as an uncertainty principle between the sparsity pattern of
		  a matrix and its row and column spaces, and use it to
		  characterize both fundamental identifiability as well as
		  (deterministic) sufficient conditions for exact recovery.
		  Our analysis is geometric in nature, with the tangent
		  spaces to the algebraic varieties of sparse and low-rank
		  matrices playing a prominent role. When the sparse and
		  low-rank matrices are drawn from certain natural random
		  ensembles, we show that the sufficient conditions for exact
		  recovery are satisfied with high probability. We conclude
		  with simulation results on synthetic matrix decomposition
		  problems.}
}

###Article{	  chasanparwil11,
  author	= {V. Chandrasekaran and S. Sanghavi and P. P. Parrilo and A.
		  S. Willsky},
  title		= {Rank-Sparsity Incoherence for Matrix Decomposition},
  journal	= siamopt,
  year		= 2011,
  number	= 2,
  volume	= 21,
  pages		= {572-596},
  doi		= {10.1137/090761793},
  abstract	= {Suppose we are given a matrix that is formed by adding an
		  unknown sparse matrix to an unknown low-rank matrix. Our
		  goal is to decompose the given matrix into its sparse and
		  low-rank components. Such a problem arises in a number of
		  applications in model and system identification, and is
		  NP-hard in general. In this paper we consider a convex
		  optimization formulation to splitting the specified matrix
		  into its components, by minimizing a linear combination of
		  the $\ell_1$ norm and the nuclear norm of the components.
		  We develop a notion of rank-sparsity incoherence, expressed
		  as an uncertainty principle between the sparsity pattern of
		  a matrix and its row and column spaces, and use it to
		  characterize both fundamental identifiability as well as
		  (deterministic) sufficient conditions for exact recovery.
		  Our analysis is geometric in nature, with the tangent
		  spaces to the algebraic varieties of sparse and low-rank
		  matrices playing a prominent role. When the sparse and
		  low-rank matrices are drawn from certain natural random
		  ensembles, we show that the sufficient conditions for exact
		  recovery are satisfied with high probability. We conclude
		  with simulation results on synthetic matrix decomposition
		  problems.}
}

###Article{	  chasanparwil11,
  author	= {V. Chandrasekaran and S. Sanghavi and P. P. Parrilo and A.
		  S. Willsky},
  title		= {Rank-Sparsity Incoherence for Matrix Decomposition},
  journal	= siamopt,
  year		= 2011,
  number	= 2,
  volume	= 21,
  pages		= {572-596},
  doi		= {10.1137/090761793},
  abstract	= {Suppose we are given a matrix that is formed by adding an
		  unknown sparse matrix to an unknown low-rank matrix. Our
		  goal is to decompose the given matrix into its sparse and
		  low-rank components. Such a problem arises in a number of
		  applications in model and system identification, and is
		  NP-hard in general. In this paper we consider a convex
		  optimization formulation to splitting the specified matrix
		  into its components, by minimizing a linear combination of
		  the $\ell_1$ norm and the nuclear norm of the components.
		  We develop a notion of rank-sparsity incoherence, expressed
		  as an uncertainty principle between the sparsity pattern of
		  a matrix and its row and column spaces, and use it to
		  characterize both fundamental identifiability as well as
		  (deterministic) sufficient conditions for exact recovery.
		  Our analysis is geometric in nature, with the tangent
		  spaces to the algebraic varieties of sparse and low-rank
		  matrices playing a prominent role. When the sparse and
		  low-rank matrices are drawn from certain natural random
		  ensembles, we show that the sufficient conditions for exact
		  recovery are satisfied with high probability. We conclude
		  with simulation results on synthetic matrix decomposition
		  problems.}
}

@Article{	  chen2014optimal,
  title		= {Optimal primal-dual methods for a class of saddle point
		  problems},
  author	= {Chen, Yunmei and Lan, Guanghui and Ouyang, Yuyuan},
  journal	= {SIAM Journal on Optimization},
  volume	= {24},
  number	= {4},
  pages		= {1779--1814},
  year		= {2014},
  publisher	= {SIAM}
}

@Article{	  chendonosaun:2001,
  author	= {S. S. Chen and D. L. Donoho and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {129--159},
  title		= {Atomic decomposition by basis pursuit},
  volume	= 43,
  year		= 2001
}

###Article{	  chendonosaun:2001,
  author	= {S. S. Chen and D. L. Donoho and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {129--159},
  title		= {Atomic decomposition by basis pursuit},
  volume	= 43,
  year		= 2001
}

###Article{	  chendonosaun:2001,
  author	= {S. S. Chen and D. L. Donoho and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {129--159},
  title		= {Atomic decomposition by basis pursuit},
  volume	= 43,
  year		= 2001
}

@Article{	  chenhuo:2006,
  author	= {J. Chen and X. Huo},
  title		= {Theoretical Results on Sparse Represenations of
		  Multiple-Measurement Vectors},
  journal	= ieeetranssigproc,
  volume	= 54,
  issue		= 12,
  year		= 2006,
  pages		= {4634--4643}
}

###Article{	  chenhuo:2006,
  author	= {J. Chen and X. Huo},
  title		= {Theoretical Results on Sparse Represenations of
		  Multiple-Measurement Vectors},
  journal	= ieeetranssigproc,
  volume	= 54,
  issue		= 12,
  year		= 2006,
  pages		= {4634--4643}
}

###Article{	  chenhuo:2006,
  author	= {J. Chen and X. Huo},
  title		= {Theoretical Results on Sparse Represenations of
		  Multiple-Measurement Vectors},
  journal	= ieeetranssigproc,
  volume	= 54,
  issue		= 12,
  year		= 2006,
  pages		= {4634--4643}
}

@InProceedings{	  chin2013runtime,
  title		= {Runtime guarantees for regression problems},
  author	= {Chin, Hui Han and Madry, Aleksander and Miller, Gary L and
		  Peng, Richard},
  booktitle	= {Proceedings of the 4th conference on Innovations in
		  Theoretical Computer Science},
  pages		= {269--282},
  year		= {2013},
  organization	= {ACM}
}

###InProceedings{ chin2013runtime,
  title		= {Runtime guarantees for regression problems},
  author	= {Chin, Hui Han and Madry, Aleksander and Miller, Gary L and
		  Peng, Richard},
  booktitle	= {Proceedings of the 4th conference on Innovations in
		  Theoretical Computer Science},
  pages		= {269--282},
  year		= {2013},
  organization	= {ACM}
}

@TechReport{	  chinflet:2001,
  address	= {UK},
  author	= {C.M. Chin and R. Fletcher},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Department of Mathematics, University of Dundee},
  number	= {NA/199},
  title		= {On the Global Convergence of an {SLP}-Filter Algorithm
		  that takes {EQP} steps},
  type		= {Numerical Analysis Report},
  year		= 2001
}

###TechReport{	  chinflet:2001,
  address	= {UK},
  author	= {C.M. Chin and R. Fletcher},
  institution	= {Department of Mathematics, University of Dundee},
  number	= {NA/199},
  title		= {On the Global Convergence of an {SLP}-Filter Algorithm
		  that takes {EQP} steps},
  type		= {Numerical Analysis Report},
  year		= 2001
}

###TechReport{	  chinflet:2001,
  address	= {UK},
  author	= {C.M. Chin and R. Fletcher},
  institution	= {Department of Mathematics, University of Dundee},
  number	= {NA/199},
  title		= {On the Global Convergence of an {SLP}-Filter Algorithm
		  that takes {EQP} steps},
  type		= {Numerical Analysis Report},
  year		= 2001
}

@Article{	  chinflet:2003,
  author	= {C.M. Chin and R. Fletcher},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  number	= 1,
  pages		= {161--177},
  title		= {On the Global Convergence of an {SLP}-Filter Algorithm
		  that takes {EQP} steps},
  volume	= 96,
  year		= 2003
}

###Article{	  chinflet:2003,
  author	= {C.M. Chin and R. Fletcher},
  journal	= mathprog,
  number	= 1,
  pages		= {161--177},
  title		= {On the Global Convergence of an {SLP}-Filter Algorithm
		  that takes {EQP} steps},
  volume	= 96,
  year		= 2003
}

###Article{	  chinflet:2003,
  author	= {C.M. Chin and R. Fletcher},
  journal	= mathprog,
  number	= 1,
  pages		= {161--177},
  title		= {On the Global Convergence of an {SLP}-Filter Algorithm
		  that takes {EQP} steps},
  volume	= 96,
  year		= 2003
}

@Article{	  cht93,
  author	= {Chen, G. and Teboulle, M.},
  title		= {Convergence analysis of a proximal-like minimization
		  algorithm using {B}regman functions},
  journal	= siamopt,
  year		= 1993,
  pages		= {538--543}
}

###Article{	  cht93,
  author	= {Chen, G. and Teboulle, M.},
  title		= {Convergence analysis of a proximal-like minimization
		  algorithm using {B}regman functions},
  journal	= siamopt,
  year		= 1993,
  pages		= {538--543}
}

###Article{	  cht93,
  author	= {Chen, G. and Teboulle, M.},
  title		= {Convergence analysis of a proximal-like minimization
		  algorithm using {B}regman functions},
  journal	= siamopt,
  year		= 1993,
  pages		= {538--543}
}

@Article{	  chung2006concentration,
  author	= {Chung, F. and Lu, L.},
  date-added	= {2012-10-30 21:08:08 +0000},
  date-modified	= {2012-10-30 21:08:08 +0000},
  journal	= {Internet Mathematics},
  number	= 1,
  pages		= {79--127},
  publisher	= {Taylor \& Francis},
  title		= {Concentration inequalities and martingale inequalities: a
		  survey},
  volume	= 3,
  year		= 2006
}

@Article{	  cjsy06,
  author	= "Carter, Michael W. and Jin, Holly H. and Saunders, Michael
		  A. and Ye, Yinyu",
  title		= "Spaseloc: {A}n adaptive subproblem algorithm for scalable
		  wireless sensor network localization",
  journal	= siamopt,
  year		= 2006,
  volume	= 17,
  number	= 4,
  page		= {1102--1128}
}

###Article{	  cjsy06,
  author	= "Carter, Michael W. and Jin, Holly H. and Saunders, Michael
		  A. and Ye, Yinyu",
  title		= "Spaseloc: {A}n adaptive subproblem algorithm for scalable
		  wireless sensor network localization",
  journal	= siamopt,
  year		= 2006,
  volume	= 17,
  number	= 4,
  page		= {1102--1128}
}

###Article{	  cjsy06,
  author	= "Carter, Michael W. and Jin, Holly H. and Saunders, Michael
		  A. and Ye, Yinyu",
  title		= "Spaseloc: {A}n adaptive subproblem algorithm for scalable
		  wireless sensor network localization",
  journal	= siamopt,
  year		= 2006,
  volume	= 17,
  number	= 4,
  page		= {1102--1128}
}

@Article{	  colehulb:1989,
  author	= {T. F. Coleman and L. A. Hulbert},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= {Math. Program., Series~B},
  number	= 3,
  pages		= {373--406},
  title		= {A direct active set algorithm for large sparse quadratic
		  programs with simple bounds},
  volume	= 45,
  year		= 1989
}

###Article{	  colehulb:1989,
  author	= {T. F. Coleman and L. A. Hulbert},
  journal	= {Math. Program., Series~B},
  number	= 3,
  pages		= {373--406},
  title		= {A direct active set algorithm for large sparse quadratic
		  programs with simple bounds},
  volume	= 45,
  year		= 1989
}

###Article{	  colehulb:1989,
  author	= {T. F. Coleman and L. A. Hulbert},
  journal	= {Math. Program., Series~B},
  number	= 3,
  pages		= {373--406},
  title		= {A direct active set algorithm for large sparse quadratic
		  programs with simple bounds},
  volume	= 45,
  year		= 1989
}

@Article{	  coleli:1996,
  abstract	= {We propose a new algorithm, a reflective Newton method,
		  for the minimization of a quadratic function of many
		  variables subject to upper and lower bounds on some of the
		  variables. The method applies to a general (indefinite)
		  quadratic function for which a local minimum subject to
		  bounds is required and is particularly suitable for the
		  large-scale problem. Our new method exhibits strong
		  convergence properties and global and second-order
		  convergence and appears to have significant practical
		  potential. Strictly feasible points are generated. We
		  provide experimental results on moderately large and sparse
		  problems based on both sparse Cholesky and preconditioned
		  conjugate gradient linear solvers.},
  author	= {T. F. Coleman and Y. Li},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {1040--1058},
  title		= {A Reflective {N}ewton Method for Minimizing a Quadratic
		  Function Subject to Bounds on Some of the Variables},
  volume	= 6,
  year		= 1996
}

###Article{	  coleli:1996,
  abstract	= {We propose a new algorithm, a reflective Newton method,
		  for the minimization of a quadratic function of many
		  variables subject to upper and lower bounds on some of the
		  variables. The method applies to a general (indefinite)
		  quadratic function for which a local minimum subject to
		  bounds is required and is particularly suitable for the
		  large-scale problem. Our new method exhibits strong
		  convergence properties and global and second-order
		  convergence and appears to have significant practical
		  potential. Strictly feasible points are generated. We
		  provide experimental results on moderately large and sparse
		  problems based on both sparse Cholesky and preconditioned
		  conjugate gradient linear solvers.},
  author	= {T. F. Coleman and Y. Li},
  journal	= siamopt,
  number	= 4,
  pages		= {1040--1058},
  title		= {A Reflective {N}ewton Method for Minimizing a Quadratic
		  Function Subject to Bounds on Some of the Variables},
  volume	= 6,
  year		= 1996
}

###Article{	  coleli:1996,
  abstract	= {We propose a new algorithm, a reflective Newton method,
		  for the minimization of a quadratic function of many
		  variables subject to upper and lower bounds on some of the
		  variables. The method applies to a general (indefinite)
		  quadratic function for which a local minimum subject to
		  bounds is required and is particularly suitable for the
		  large-scale problem. Our new method exhibits strong
		  convergence properties and global and second-order
		  convergence and appears to have significant practical
		  potential. Strictly feasible points are generated. We
		  provide experimental results on moderately large and sparse
		  problems based on both sparse Cholesky and preconditioned
		  conjugate gradient linear solvers.},
  author	= {T. F. Coleman and Y. Li},
  journal	= siamopt,
  number	= 4,
  pages		= {1040--1058},
  title		= {A Reflective {N}ewton Method for Minimizing a Quadratic
		  Function Subject to Bounds on Some of the Variables},
  volume	= 6,
  year		= 1996
}

@Article{	  comwaj2005,
  author	= {Patrick L. Combettes and Val\'erie R. Wajs},
  title		= {Signal recovery by proximal forward-backward splitting},
  journal	= siammms,
  year		= 2005,
  volume	= 4,
  number	= 4,
  pages		= {1168-1200}
}

###Article{	  comwaj2005,
  author	= {Patrick L. Combettes and Val\'erie R. Wajs},
  title		= {Signal recovery by proximal forward-backward splitting},
  journal	= siammms,
  year		= 2005,
  volume	= 4,
  number	= 4,
  pages		= {1168-1200}
}

###Article{	  comwaj2005,
  author	= {Patrick L. Combettes and Val\'erie R. Wajs},
  title		= {Signal recovery by proximal forward-backward splitting},
  journal	= siammms,
  year		= 2005,
  volume	= 4,
  number	= 4,
  pages		= {1168-1200}
}

@InProceedings{	  conf/icml/barbero11,
  author	= {Barbero, \'Alvaro and Sra, Suvrit},
  booktitle	= {Intern. Conf. on Machine Learning},
  editor	= {Getoor, Lise and Scheffer, Tobias},
  pages		= {313-320},
  publisher	= {Omnipress},
  title		= {Fast {Newton}-type Methods for Total Variation
		  Regularization.},
  year		= 2011
}

@Article{	  conngoulsarttoin:1996,
  author	= {A. R. Conn and N. I. M. Gould and A. Sartenaer and
		  {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {674-703},
  title		= {Convergence properties of an augmented {L}agrangian
		  algorithm for optimization with a combination of general
		  equality and linear constraints},
  volume	= 6,
  year		= 1996
}

###Article{	  conngoulsarttoin:1996,
  author	= {A. R. Conn and N. I. M. Gould and A. Sartenaer and
		  {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {674-703},
  title		= {Convergence properties of an augmented {L}agrangian
		  algorithm for optimization with a combination of general
		  equality and linear constraints},
  volume	= 6,
  year		= 1996
}

###Article{	  conngoulsarttoin:1996,
  author	= {A. R. Conn and N. I. M. Gould and A. Sartenaer and
		  {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {674-703},
  title		= {Convergence properties of an augmented {L}agrangian
		  algorithm for optimization with a combination of general
		  equality and linear constraints},
  volume	= 6,
  year		= 1996
}

@Article{	  conngoultoin:1988,
  author	= {A. R. Conn and N. I. M. Gould and \mbox{Ph}. L. Toint},
  abstract	= {We describe the results of a series of tests upon a class
		  of new methods of trust region type for solving the simple
		  bound constrained minimization problem. The results are
		  encouraging and lead us to believe that the method will
		  prove useful in solving large problems.},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathcomp,
  pages		= {399--430},
  summary	= {The results of tests on the trust-region methods proposed
		  by \citebb{ConnGoulToin88a} for solving the bound
		  constrained minimization problem are discussed.},
  title		= {Testing a class of methods for solving minimization
		  problems with simple bounds on the variables},
  volume	= 50,
  year		= 1988
}

###Article{	  conngoultoin:1988,
  author	= {A. R. Conn and N. I. M. Gould and \mbox{Ph}. L. Toint},
  abstract	= {We describe the results of a series of tests upon a class
		  of new methods of trust region type for solving the simple
		  bound constrained minimization problem. The results are
		  encouraging and lead us to believe that the method will
		  prove useful in solving large problems.},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathcomp,
  pages		= {399--430},
  summary	= {The results of tests on the trust-region methods proposed
		  by \citebb{ConnGoulToin88a} for solving the bound
		  constrained minimization problem are discussed.},
  title		= {Testing a class of methods for solving minimization
		  problems with simple bounds on the variables},
  volume	= 50,
  year		= 1988
}

###Article{	  conngoultoin:1988,
  author	= {A. R. Conn and N. I. M. Gould and \mbox{Ph}. L. Toint},
  abstract	= {We describe the results of a series of tests upon a class
		  of new methods of trust region type for solving the simple
		  bound constrained minimization problem. The results are
		  encouraging and lead us to believe that the method will
		  prove useful in solving large problems.},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathcomp,
  pages		= {399--430},
  summary	= {The results of tests on the trust-region methods proposed
		  by \citebb{ConnGoulToin88a} for solving the bound
		  constrained minimization problem are discussed.},
  title		= {Testing a class of methods for solving minimization
		  problems with simple bounds on the variables},
  volume	= 50,
  year		= 1988
}

@Article{	  conngoultoin:1991,
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  journal	= siamnumanal,
  month		= {April},
  number	= 2,
  pages		= {545--572},
  title		= {A Globally Convergent Augmented {L}agrangian Algorithm for
		  Optimization with General Constraints and Simple Bounds},
  volume	= 28,
  year		= 1991
}

###Article{	  conngoultoin:1991,
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  journal	= siamnumanal,
  month		= {April},
  number	= 2,
  pages		= {545--572},
  title		= {A Globally Convergent Augmented {L}agrangian Algorithm for
		  Optimization with General Constraints and Simple Bounds},
  volume	= 28,
  year		= 1991
}

###Article{	  conngoultoin:1991,
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  journal	= siamnumanal,
  month		= {April},
  number	= 2,
  pages		= {545--572},
  title		= {A Globally Convergent Augmented {L}agrangian Algorithm for
		  Optimization with General Constraints and Simple Bounds},
  volume	= 28,
  year		= 1991
}

@Book{		  conngoultoin:1992,
  address	= {Berlin},
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-10-22 16:04:53 -0700},
  publisher	= springer,
  series	= {Springer Series in Computational Mathematics},
  title		= {{\sf LANCELOT}: a {F}ortran package for Large-scale
		  Nonlinear Optimization ({R}elease {A})},
  year		= 1992
}

###Book{	  conngoultoin:1992,
  address	= {Berlin},
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-10-22 16:04:53 -0700},
  publisher	= springer,
  series	= {Springer Series in Computational Mathematics},
  title		= {{\sf LANCELOT}: a {F}ortran package for Large-scale
		  Nonlinear Optimization ({R}elease {A})},
  year		= 1992
}

###Book{	  conngoultoin:1992,
  address	= {Berlin},
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-10-22 16:04:53 -0700},
  publisher	= springer,
  series	= {Springer Series in Computational Mathematics},
  title		= {{\sf LANCELOT}: a {F}ortran package for Large-scale
		  Nonlinear Optimization ({R}elease {A})},
  year		= 1992
}

@InProceedings{	  conngoultoin:1992a,
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  booktitle	= {Numerical Analysis 1991, Pitman Res. Notes Math. Ser.},
  date-modified	= {2007-10-13 18:35:11 -0700},
  editor	= {D. F. Griffiths and G. A. Watson},
  pages		= {49--68},
  publisher	= {Longman Scientific {\&} Technical, Harlow, UK},
  title		= {On the number of inner iterations per outer iteration of a
		  globally convergent algorithm for optimization with general
		  nonlinear equality constraints and simple bounds},
  year		= 1992
}

###InProceedings{ conngoultoin:1992a,
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  booktitle	= {Numerical Analysis 1991, Pitman Res. Notes Math. Ser.},
  date-modified	= {2007-10-13 18:35:11 -0700},
  editor	= {D. F. Griffiths and G. A. Watson},
  pages		= {49--68},
  publisher	= {Longman Scientific {\&} Technical, Harlow, UK},
  title		= {On the number of inner iterations per outer iteration of a
		  globally convergent algorithm for optimization with general
		  nonlinear equality constraints and simple bounds},
  year		= 1992
}

###InProceedings{ conngoultoin:1992a,
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  booktitle	= {Numerical Analysis 1991, Pitman Res. Notes Math. Ser.},
  date-modified	= {2007-10-13 18:35:11 -0700},
  editor	= {D. F. Griffiths and G. A. Watson},
  pages		= {49--68},
  publisher	= {Longman Scientific {\&} Technical, Harlow, UK},
  title		= {On the number of inner iterations per outer iteration of a
		  globally convergent algorithm for optimization with general
		  nonlinear equality constraints and simple bounds},
  year		= 1992
}

@Article{	  conngoultoin:1997,
  abstract	= {This paper considers the number of inner iterations
		  required per outeriteration for the algorithm proposed by
		  Conn et al.{$[$}9{$]$}. We show that asymptotically, under
		  suitable reasonable assumptions, a single inner iteration
		  suffices.},
  author	= {A. R. Conn and N. Gould and {\mbox{Ph}}. L. Toint},
  date-added	= {2007-10-13 18:38:05 -0700},
  date-modified	= {2007-10-22 16:05:40 -0700},
  journal	= {Computational Optimization and Applications},
  m3		= {10.1023/A:1008667728545},
  number	= 1,
  pages		= {41--69},
  title		= {On the Number of Inner Iterations Per Outer Iteration of a
		  Globally Convergent Algorithm for Optimization with General
		  Nonlinear Inequality Constraints and Simple Bounds},
  ty		= {JOUR},
  url		= {http://dx.doi.org/10.1023/A:1008667728545},
  volume	= 7,
  year		= 1997
}

###Article{	  conngoultoin:1997,
  abstract	= {This paper considers the number of inner iterations
		  required per outeriteration for the algorithm proposed by
		  Conn et al.{$[$}9{$]$}. We show that asymptotically, under
		  suitable reasonable assumptions, a single inner iteration
		  suffices.},
  author	= {A. R. Conn and N. Gould and {\mbox{Ph}}. L. Toint},
  date-added	= {2007-10-13 18:38:05 -0700},
  date-modified	= {2007-10-22 16:05:40 -0700},
  journal	= {Computational Optimization and Applications},
  m3		= {10.1023/A:1008667728545},
  number	= 1,
  pages		= {41--69},
  title		= {On the Number of Inner Iterations Per Outer Iteration of a
		  Globally Convergent Algorithm for Optimization with General
		  Nonlinear Inequality Constraints and Simple Bounds},
  ty		= {JOUR},
  url		= {http://dx.doi.org/10.1023/A:1008667728545},
  volume	= 7,
  year		= 1997
}

###Article{	  conngoultoin:1997,
  abstract	= {This paper considers the number of inner iterations
		  required per outeriteration for the algorithm proposed by
		  Conn et al.{$[$}9{$]$}. We show that asymptotically, under
		  suitable reasonable assumptions, a single inner iteration
		  suffices.},
  author	= {A. R. Conn and N. Gould and {\mbox{Ph}}. L. Toint},
  date-added	= {2007-10-13 18:38:05 -0700},
  date-modified	= {2007-10-22 16:05:40 -0700},
  journal	= {Computational Optimization and Applications},
  m3		= {10.1023/A:1008667728545},
  number	= 1,
  pages		= {41--69},
  title		= {On the Number of Inner Iterations Per Outer Iteration of a
		  Globally Convergent Algorithm for Optimization with General
		  Nonlinear Inequality Constraints and Simple Bounds},
  ty		= {JOUR},
  url		= {http://dx.doi.org/10.1023/A:1008667728545},
  volume	= 7,
  year		= 1997
}

@Book{		  conngoultoin:2000,
  address	= {Philadelphia},
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= siampub,
  series	= {{MPS-SIAM} Series on Optimization},
  title		= {Trust-Region Methods},
  year		= 2000
}

###Book{	  conngoultoin:2000,
  address	= {Philadelphia},
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= siampub,
  series	= {{MPS-SIAM} Series on Optimization},
  title		= {Trust-Region Methods},
  year		= 2000
}

###Book{	  conngoultoin:2000,
  address	= {Philadelphia},
  author	= {A. R. Conn and N. I. M. Gould and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= siampub,
  series	= {{MPS-SIAM} Series on Optimization},
  title		= {Trust-Region Methods},
  year		= 2000
}

@TechReport{	  connsinc:1975,
  author	= {A. R. Conn and J. W. Sinclair},
  title		= {Quadratic programming via a nondifferentiable penalty
		  function},
  institution	= {Faculty of Mathematics, University of Waterloo, Waterloo},
  year		= 1975,
  type		= {Tech. Rep.},
  number	= {CORR 75/15}
}

###TechReport{	  connsinc:1975,
  author	= {A. R. Conn and J. W. Sinclair},
  title		= {Quadratic programming via a nondifferentiable penalty
		  function},
  institution	= {Faculty of Mathematics, University of Waterloo, Waterloo},
  year		= 1975,
  type		= {Tech. Rep.},
  number	= {CORR 75/15}
}

###TechReport{	  connsinc:1975,
  author	= {A. R. Conn and J. W. Sinclair},
  title		= {Quadratic programming via a nondifferentiable penalty
		  function},
  institution	= {Faculty of Mathematics, University of Waterloo, Waterloo},
  year		= 1975,
  type		= {Tech. Rep.},
  number	= {CORR 75/15}
}

@Book{		  cormleisrivestei:2001,
  address	= {Cambridge, Massachussetts},
  author	= {Thomas H. Cormen and Charles E. Leiserson and Ronald L.
		  Rivest and Clifford Stein},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {Second},
  month		= {September},
  publisher	= {MIT Press},
  title		= {Introduction to Algorithms},
  year		= 2001
}

###Book{	  cormleisrivestei:2001,
  address	= {Cambridge, Massachussetts},
  author	= {Thomas H. Cormen and Charles E. Leiserson and Ronald L.
		  Rivest and Clifford Stein},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {Second},
  month		= {September},
  publisher	= {MIT Press},
  title		= {Introduction to Algorithms},
  year		= 2001
}

###Book{	  cormleisrivestei:2001,
  address	= {Cambridge, Massachussetts},
  author	= {Thomas H. Cormen and Charles E. Leiserson and Ronald L.
		  Rivest and Clifford Stein},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {Second},
  month		= {September},
  publisher	= {MIT Press},
  title		= {Introduction to Algorithms},
  year		= 2001
}

@Article{	  correa1993convergence,
  title		= {Convergence of some algorithms for convex minimization},
  author	= {Correa, Rafael and Lemar{\'e}chal, Claude},
  journal	= mathprog,
  volume	= 62,
  number	= {1-3},
  pages		= {261--275},
  year		= 1993,
  publisher	= {Springer}
}

###Article{	  correa1993convergence,
  title		= {Convergence of some algorithms for convex minimization},
  author	= {Correa, Rafael and Lemar{\'e}chal, Claude},
  journal	= mathprog,
  volume	= 62,
  number	= {1-3},
  pages		= {261--275},
  year		= 1993,
  publisher	= {Springer}
}

@Article{	  cottraoengakreu:2005,
  author	= {S. F. Cotter and B. D. Rao and K. Engan and K.
		  Kreutz-Delgado},
  title		= {Sparse Solutions to Linear Inverse Problems with Multiple
		  Measurement Vectors},
  journal	= ieeetranssigproc,
  volume	= 53,
  issue		= 7,
  year		= 2005,
  pages		= {2477--2488}
}

###Article{	  cottraoengakreu:2005,
  author	= {S. F. Cotter and B. D. Rao and K. Engan and K.
		  Kreutz-Delgado},
  title		= {Sparse Solutions to Linear Inverse Problems with Multiple
		  Measurement Vectors},
  journal	= ieeetranssigproc,
  volume	= 53,
  issue		= 7,
  year		= 2005,
  pages		= {2477--2488}
}

###Article{	  cottraoengakreu:2005,
  author	= {S. F. Cotter and B. D. Rao and K. Engan and K.
		  Kreutz-Delgado},
  title		= {Sparse Solutions to Linear Inverse Problems with Multiple
		  Measurement Vectors},
  journal	= ieeetranssigproc,
  volume	= 53,
  issue		= 7,
  year		= 2005,
  pages		= {2477--2488}
}

@Article{	  cour:1943,
  author	= {Richard Courant},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= bullams,
  pages		= {1--23},
  title		= {Variational methods for the solution of problems with
		  equilibrium and variation},
  volume	= 49,
  year		= 1943
}

###Article{	  cour:1943,
  author	= {Richard Courant},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= bullams,
  pages		= {1--23},
  title		= {Variational methods for the solution of problems with
		  equilibrium and variation},
  volume	= 49,
  year		= 1943
}

###Article{	  cour:1943,
  author	= {Richard Courant},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= bullams,
  pages		= {1--23},
  title		= {Variational methods for the solution of problems with
		  equilibrium and variation},
  volume	= 49,
  year		= 1943
}

@Book{		  covethom:1991,
  address	= {New York},
  author	= {Thomas Cover and Joy Thomas},
  date-modified	= {2007-07-18 14:59:32 -0700},
  publisher	= {Wiley},
  title		= {Elements of Information Theory},
  year		= 1991
}

###Book{	  covethom:1991,
  address	= {New York},
  author	= {Thomas Cover and Joy Thomas},
  date-modified	= {2007-07-18 14:59:32 -0700},
  publisher	= {Wiley},
  title		= {Elements of Information Theory},
  year		= 1991
}

###Book{	  covethom:1991,
  address	= {New York},
  author	= {Thomas Cover and Joy Thomas},
  date-modified	= {2007-07-18 14:59:32 -0700},
  publisher	= {Wiley},
  title		= {Elements of Information Theory},
  year		= 1991
}

@Misc{		  cplex:2007,
  howpublished	= {Mathematical programming system,
		  \url{http://www.cplex.com}},
  key		= {CPLEX},
  title		= {{ILOG CPLEX}},
  year		= 2007
}

###Misc{	  cplex:2007,
  howpublished	= {Mathematical programming system,
		  \url{http://www.cplex.com}},
  key		= {CPLEX},
  title		= {{ILOG CPLEX}},
  year		= 2007
}

###Misc{	  cplex:2007,
  howpublished	= {Mathematical programming system,
		  \url{http://www.cplex.com}},
  key		= {CPLEX},
  title		= {{ILOG CPLEX}},
  year		= 2007
}

@Article{	  crt06a,
  abstract	= {This paper considers the model problem of reconstructing
		  an object from incomplete frequency samples. Consider a
		  discrete-time signal f/spl isin/C/sup N/ and a randomly
		  chosen set of frequencies /spl Omega/. Is it possible to
		  reconstruct f from the partial knowledge of its Fourier
		  coefficients on the set /spl Omega/? A typical result of
		  this paper is as follows. Suppose that f is a superposition
		  of |T| spikes f(t)=/spl sigma//sub /spl tau//spl
		  isin/T/f(/spl tau/)/spl delta/(t-/spl tau/) obeying |T|/spl
		  les/C/sub M//spl middot/(log N)/sup -1/ /spl middot/ |/spl
		  Omega/| for some constant C/sub M/>0. We do not know the
		  locations of the spikes nor their amplitudes. Then with
		  probability at least 1-O(N/sup -M/), f can be reconstructed
		  exactly as the solution to the /spl lscr//sub 1/
		  minimization problem. In short, exact recovery may be
		  obtained by solving a convex optimization problem. We give
		  numerical values for C/sub M/ which depend on the desired
		  probability of success. Our result may be interpreted as a
		  novel kind of nonlinear sampling theorem. In effect, it
		  says that any signal made out of |T| spikes may be
		  recovered by convex programming from almost every set of
		  frequencies of size O(|T|/spl middot/logN). Moreover, this
		  is nearly optimal in the sense that any method succeeding
		  with probability 1-O(N/sup -M/) would in general require a
		  number of frequency samples at least proportional to
		  |T|/spl middot/logN. The methodology extends to a variety
		  of other situations and higher dimensions. For example, we
		  show how one can reconstruct a piecewise constant (one- or
		  two-dimensional) object from incomplete frequency samples -
		  provided that the number of jumps (discontinuities) obeys
		  the condition above - by minimizing other convex
		  functionals such as the total variation of f.},
  author	= {E. J. Cand\`{e}s and J. Romberg and T. Tao},
  journal	= ieeetransinfo,
  month		= {February},
  number	= 2,
  pages		= {489-509},
  title		= {Robust uncertainty principles: exact signal reconstruction
		  from highly incomplete frequency information},
  volume	= 52,
  year		= 2006
}

###Article{	  crt06a,
  abstract	= {This paper considers the model problem of reconstructing
		  an object from incomplete frequency samples. Consider a
		  discrete-time signal f/spl isin/C/sup N/ and a randomly
		  chosen set of frequencies /spl Omega/. Is it possible to
		  reconstruct f from the partial knowledge of its Fourier
		  coefficients on the set /spl Omega/? A typical result of
		  this paper is as follows. Suppose that f is a superposition
		  of |T| spikes f(t)=/spl sigma//sub /spl tau//spl
		  isin/T/f(/spl tau/)/spl delta/(t-/spl tau/) obeying |T|/spl
		  les/C/sub M//spl middot/(log N)/sup -1/ /spl middot/ |/spl
		  Omega/| for some constant C/sub M/>0. We do not know the
		  locations of the spikes nor their amplitudes. Then with
		  probability at least 1-O(N/sup -M/), f can be reconstructed
		  exactly as the solution to the /spl lscr//sub 1/
		  minimization problem. In short, exact recovery may be
		  obtained by solving a convex optimization problem. We give
		  numerical values for C/sub M/ which depend on the desired
		  probability of success. Our result may be interpreted as a
		  novel kind of nonlinear sampling theorem. In effect, it
		  says that any signal made out of |T| spikes may be
		  recovered by convex programming from almost every set of
		  frequencies of size O(|T|/spl middot/logN). Moreover, this
		  is nearly optimal in the sense that any method succeeding
		  with probability 1-O(N/sup -M/) would in general require a
		  number of frequency samples at least proportional to
		  |T|/spl middot/logN. The methodology extends to a variety
		  of other situations and higher dimensions. For example, we
		  show how one can reconstruct a piecewise constant (one- or
		  two-dimensional) object from incomplete frequency samples -
		  provided that the number of jumps (discontinuities) obeys
		  the condition above - by minimizing other convex
		  functionals such as the total variation of f.},
  author	= {E. J. Cand\`{e}s and J. Romberg and T. Tao},
  journal	= ieeetransinfo,
  month		= {February},
  number	= 2,
  pages		= {489-509},
  title		= {Robust uncertainty principles: exact signal reconstruction
		  from highly incomplete frequency information},
  volume	= 52,
  year		= 2006
}

###Article{	  crt06a,
  abstract	= {This paper considers the model problem of reconstructing
		  an object from incomplete frequency samples. Consider a
		  discrete-time signal f/spl isin/C/sup N/ and a randomly
		  chosen set of frequencies /spl Omega/. Is it possible to
		  reconstruct f from the partial knowledge of its Fourier
		  coefficients on the set /spl Omega/? A typical result of
		  this paper is as follows. Suppose that f is a superposition
		  of |T| spikes f(t)=/spl sigma//sub /spl tau//spl
		  isin/T/f(/spl tau/)/spl delta/(t-/spl tau/) obeying |T|/spl
		  les/C/sub M//spl middot/(log N)/sup -1/ /spl middot/ |/spl
		  Omega/| for some constant C/sub M/>0. We do not know the
		  locations of the spikes nor their amplitudes. Then with
		  probability at least 1-O(N/sup -M/), f can be reconstructed
		  exactly as the solution to the /spl lscr//sub 1/
		  minimization problem. In short, exact recovery may be
		  obtained by solving a convex optimization problem. We give
		  numerical values for C/sub M/ which depend on the desired
		  probability of success. Our result may be interpreted as a
		  novel kind of nonlinear sampling theorem. In effect, it
		  says that any signal made out of |T| spikes may be
		  recovered by convex programming from almost every set of
		  frequencies of size O(|T|/spl middot/logN). Moreover, this
		  is nearly optimal in the sense that any method succeeding
		  with probability 1-O(N/sup -M/) would in general require a
		  number of frequency samples at least proportional to
		  |T|/spl middot/logN. The methodology extends to a variety
		  of other situations and higher dimensions. For example, we
		  show how one can reconstruct a piecewise constant (one- or
		  two-dimensional) object from incomplete frequency samples -
		  provided that the number of jumps (discontinuities) obeys
		  the condition above - by minimizing other convex
		  functionals such as the total variation of f.},
  author	= {E. J. Cand\`{e}s and J. Romberg and T. Tao},
  journal	= ieeetransinfo,
  month		= {February},
  number	= 2,
  pages		= {489-509},
  title		= {Robust uncertainty principles: exact signal reconstruction
		  from highly incomplete frequency information},
  volume	= 52,
  year		= 2006
}

@Article{	  crt06b,
  author	= {E. J. Cand\`{e}s and J. Romberg and T. Tao},
  journal	= {Comm. Pure Appl. Math.},
  number	= 8,
  pages		= {1207-1223},
  title		= {Stable Signal Recovery from Incomplete and Inaccurate
		  Measurements},
  volume	= 59,
  year		= 2006
}

###Article{	  crt06b,
  author	= {E. J. Cand\`{e}s and J. Romberg and T. Tao},
  journal	= {Comm. Pure Appl. Math.},
  number	= 8,
  pages		= {1207-1223},
  title		= {Stable Signal Recovery from Incomplete and Inaccurate
		  Measurements},
  volume	= 59,
  year		= 2006
}

###Article{	  crt06b,
  author	= {E. J. Cand\`{e}s and J. Romberg and T. Tao},
  journal	= {Comm. Pure Appl. Math.},
  number	= 8,
  pages		= {1207-1223},
  title		= {Stable Signal Recovery from Incomplete and Inaccurate
		  Measurements},
  volume	= 59,
  year		= 2006
}

@Article{	  curtisnocedal:2008,
  journal	= {IMA Journal of Numerical Analysis},
  year		= 2008,
  volume	= 28,
  pages		= {749−769},
  doi		= {10.1093/imanum/drn003},
  title		= {Flexible penalty functions for nonlinear constrained
		  optimization},
  author	= {Frank E. Curtis and Jorge Nocedal}
}

###Article{	  curtisnocedal:2008,
  journal	= {IMA Journal of Numerical Analysis},
  year		= 2008,
  volume	= 28,
  pages		= {749−769},
  doi		= {10.1093/imanum/drn003},
  title		= {Flexible penalty functions for nonlinear constrained
		  optimization},
  author	= {Frank E. Curtis and Jorge Nocedal}
}

###Article{	  curtisnocedal:2008,
  journal	= {IMA Journal of Numerical Analysis},
  year		= 2008,
  volume	= 28,
  pages		= {749−769},
  doi		= {10.1093/imanum/drn003},
  title		= {Flexible penalty functions for nonlinear constrained
		  optimization},
  author	= {Frank E. Curtis and Jorge Nocedal}
}

@Misc{		  curvelet,
  author	= {E. J. Cand\`{e}s and L. Demanet and D. L. Donoho and L.-X.
		  Ying},
  howpublished	= {\url{http://www.curvelet.org/}},
  title		= {{CurveLab}},
  year		= 2007
}

###Misc{	  curvelet,
  author	= {E. J. Cand\`{e}s and L. Demanet and D. L. Donoho and L.-X.
		  Ying},
  howpublished	= {\url{http://www.curvelet.org/}},
  title		= {{CurveLab}},
  year		= 2007
}

###Misc{	  curvelet,
  author	= {E. J. Cand\`{e}s and L. Demanet and D. L. Donoho and L.-X.
		  Ying},
  howpublished	= {\url{http://www.curvelet.org/}},
  title		= {{CurveLab}},
  year		= 2007
}

@Misc{		  cvx:2009,
  author	= {M. Grant and S. Boyd},
  title		= {{CVX: Matlab} software for disciplined convex programming
		  (web page and software)},
  howpublished	= {\url{http://stanford.edu/~boyd/cvx}},
  month		= {February},
  year		= 2009
}

###Misc{	  cvx:2009,
  author	= {M. Grant and S. Boyd},
  title		= {{CVX: Matlab} software for disciplined convex programming
		  (web page and software)},
  howpublished	= {\url{http://stanford.edu/~boyd/cvx}},
  month		= {February},
  year		= 2009
}

###Misc{	  cvx:2009,
  author	= {M. Grant and S. Boyd},
  title		= {{CVX: Matlab} software for disciplined convex programming
		  (web page and software)},
  howpublished	= {\url{http://stanford.edu/~boyd/cvx}},
  month		= {February},
  year		= 2009
}

@TechReport{	  dag08,
  author	= {M. E. Davies and R. Gribonval},
  title		= {Restricted isometry constants where $\ell^p$ sparse
		  recovery can fail for $0<p\le1$},
  institution	= {Institut de Recherche en Informatique et Syst\'emes
		  Al\'eatoires},
  year		= 2008,
  type		= {Publication interne},
  number	= 1899,
  month		= {July}
}

###TechReport{	  dag08,
  author	= {M. E. Davies and R. Gribonval},
  title		= {Restricted isometry constants where $\ell^p$ sparse
		  recovery can fail for $0<p\le1$},
  institution	= {Institut de Recherche en Informatique et Syst\'emes
		  Al\'eatoires},
  year		= 2008,
  type		= {Publication interne},
  number	= 1899,
  month		= {July}
}

###TechReport{	  dag08,
  author	= {M. E. Davies and R. Gribonval},
  title		= {Restricted isometry constants where $\ell^p$ sparse
		  recovery can fail for $0<p\le1$},
  institution	= {Institut de Recherche en Informatique et Syst\'emes
		  Al\'eatoires},
  year		= 2008,
  type		= {Publication interne},
  number	= 1899,
  month		= {July}
}

@Article{	  daiflet:2005,
  author	= {Y.-H. Dai and R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= numermath,
  pages		= {21-47},
  title		= {Projected {B}arzilai-{B}orwein methods for large-scale
		  box-constrained quadratic programming},
  volume	= 100,
  year		= 2005
}

###Article{	  daiflet:2005,
  author	= {Y.-H. Dai and R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= numermath,
  pages		= {21-47},
  title		= {Projected {B}arzilai-{B}orwein methods for large-scale
		  box-constrained quadratic programming},
  volume	= 100,
  year		= 2005
}

###Article{	  daiflet:2005,
  author	= {Y.-H. Dai and R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= numermath,
  pages		= {21-47},
  title		= {Projected {B}arzilai-{B}orwein methods for large-scale
		  box-constrained quadratic programming},
  volume	= 100,
  year		= 2005
}

@Article{	  daihageschitzhan:2006,
  author	= {Y. Dai and W. W. Hager and K. Schittkowski and H. Zhang},
  date-added	= {2008-01-10 11:07:30 -0800},
  date-modified	= {2008-01-10 11:08:16 -0800},
  title		= {The cyclic {Barzilai-Borwein} method for unconstrained
		  optimization},
  journal	= imanumerana,
  year		= 2006,
  volume	= 7,
  pages		= {604--627}
}

###Article{	  daihageschitzhan:2006,
  author	= {Y. Dai and W. W. Hager and K. Schittkowski and H. Zhang},
  date-added	= {2008-01-10 11:07:30 -0800},
  date-modified	= {2008-01-10 11:08:16 -0800},
  title		= {The cyclic {Barzilai-Borwein} method for unconstrained
		  optimization},
  journal	= imanumerana,
  year		= 2006,
  volume	= 7,
  pages		= {604--627}
}

###Article{	  daihageschitzhan:2006,
  author	= {Y. Dai and W. W. Hager and K. Schittkowski and H. Zhang},
  date-added	= {2008-01-10 11:07:30 -0800},
  date-modified	= {2008-01-10 11:08:16 -0800},
  title		= {The cyclic {Barzilai-Borwein} method for unconstrained
		  optimization},
  journal	= imanumerana,
  year		= 2006,
  volume	= 7,
  pages		= {604--627}
}

@Article{	  dani:1973,
  author	= {James W. Daniel},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= numermath,
  pages		= {381-387},
  title		= {{N}ewton's {M}ethod for Nonlinear Inequalities},
  volume	= 21,
  year		= 1973
}

###Article{	  dani:1973,
  author	= {James W. Daniel},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= numermath,
  pages		= {381-387},
  title		= {{N}ewton's {M}ethod for Nonlinear Inequalities},
  volume	= 21,
  year		= 1973
}

###Article{	  dani:1973,
  author	= {James W. Daniel},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= numermath,
  pages		= {381-387},
  title		= {{N}ewton's {M}ethod for Nonlinear Inequalities},
  volume	= 21,
  year		= 1973
}

@Book{		  dantzig:1998,
  abstract	= {In real-world problems related to finance, business, and
		  management, mathematicians and economists frequently
		  encounter optimization problems. In this classic book,
		  George Dantzig looks at a wealth of examples and develops
		  linear programming methods for their solutions. He begins
		  by introducing the basic theory of linear inequalities and
		  describes the powerful simplex method used to solve them.
		  Treatments of the price concept, the transportation
		  problem, and matrix methods are also given, and key
		  mathematical concepts such as the properties of convex sets
		  and linear vector spaces are covered. George Dantzig is
		  properly acclaimed as the "father of linear programming."
		  Linear programming is a mathematical technique used to
		  optimize a situation. It can be used to minimize traffic
		  congestion or to maximize the scheduling of airline
		  flights. He formulated its basic theoretical model and
		  discovered its underlying computational algorithm, the
		  "simplex method," in a pathbreaking memorandum published by
		  the United States Air Force in early 1948. {\it Linear
		  Programming and Extensions} provides an extraordinary
		  account of the subsequent development of his subject,
		  including research in mathematical theory, computation,
		  economic analysis, and applications to industrial problems.
		  Dantzig first achieved success as a statistics graduate
		  student at the University of California, Berkeley. One day
		  he arrived for a class after it had begun, and assumed the
		  two problems on the board were assigned for homework. When
		  he handed in the solutions, he apologized to his professor,
		  Jerzy Neyman, for their being late but explained that he
		  had found the problems harder than usual. About six weeks
		  later, Neyman excitedly told Dantzig, "I've just written an
		  introduction to one of your papers. Read it so I can send
		  it out right away for publication." Dantzig had no idea
		  what he was talking about. He later learned that the
		  "homework" problems had in fact been two famous unsolved
		  problems in statistics.},
  author	= {Dantzig, George B.},
  isbn		= 0691059136,
  keywords	= {linear, programming},
  month		= {August},
  publisher	= {Princeton University Press},
  title		= {Linear Programming and Extensions},
  year		= 1998
}

###Book{	  dantzig:1998,
  abstract	= {In real-world problems related to finance, business, and
		  management, mathematicians and economists frequently
		  encounter optimization problems. In this classic book,
		  George Dantzig looks at a wealth of examples and develops
		  linear programming methods for their solutions. He begins
		  by introducing the basic theory of linear inequalities and
		  describes the powerful simplex method used to solve them.
		  Treatments of the price concept, the transportation
		  problem, and matrix methods are also given, and key
		  mathematical concepts such as the properties of convex sets
		  and linear vector spaces are covered. George Dantzig is
		  properly acclaimed as the "father of linear programming."
		  Linear programming is a mathematical technique used to
		  optimize a situation. It can be used to minimize traffic
		  congestion or to maximize the scheduling of airline
		  flights. He formulated its basic theoretical model and
		  discovered its underlying computational algorithm, the
		  "simplex method," in a pathbreaking memorandum published by
		  the United States Air Force in early 1948. {\it Linear
		  Programming and Extensions} provides an extraordinary
		  account of the subsequent development of his subject,
		  including research in mathematical theory, computation,
		  economic analysis, and applications to industrial problems.
		  Dantzig first achieved success as a statistics graduate
		  student at the University of California, Berkeley. One day
		  he arrived for a class after it had begun, and assumed the
		  two problems on the board were assigned for homework. When
		  he handed in the solutions, he apologized to his professor,
		  Jerzy Neyman, for their being late but explained that he
		  had found the problems harder than usual. About six weeks
		  later, Neyman excitedly told Dantzig, "I've just written an
		  introduction to one of your papers. Read it so I can send
		  it out right away for publication." Dantzig had no idea
		  what he was talking about. He later learned that the
		  "homework" problems had in fact been two famous unsolved
		  problems in statistics.},
  author	= {Dantzig, George B.},
  isbn		= 0691059136,
  keywords	= {linear, programming},
  month		= {August},
  publisher	= {Princeton University Press},
  title		= {Linear Programming and Extensions},
  year		= 1998
}

###Book{	  dantzig:1998,
  abstract	= {In real-world problems related to finance, business, and
		  management, mathematicians and economists frequently
		  encounter optimization problems. In this classic book,
		  George Dantzig looks at a wealth of examples and develops
		  linear programming methods for their solutions. He begins
		  by introducing the basic theory of linear inequalities and
		  describes the powerful simplex method used to solve them.
		  Treatments of the price concept, the transportation
		  problem, and matrix methods are also given, and key
		  mathematical concepts such as the properties of convex sets
		  and linear vector spaces are covered. George Dantzig is
		  properly acclaimed as the "father of linear programming."
		  Linear programming is a mathematical technique used to
		  optimize a situation. It can be used to minimize traffic
		  congestion or to maximize the scheduling of airline
		  flights. He formulated its basic theoretical model and
		  discovered its underlying computational algorithm, the
		  "simplex method," in a pathbreaking memorandum published by
		  the United States Air Force in early 1948. {\it Linear
		  Programming and Extensions} provides an extraordinary
		  account of the subsequent development of his subject,
		  including research in mathematical theory, computation,
		  economic analysis, and applications to industrial problems.
		  Dantzig first achieved success as a statistics graduate
		  student at the University of California, Berkeley. One day
		  he arrived for a class after it had begun, and assumed the
		  two problems on the board were assigned for homework. When
		  he handed in the solutions, he apologized to his professor,
		  Jerzy Neyman, for their being late but explained that he
		  had found the problems harder than usual. About six weeks
		  later, Neyman excitedly told Dantzig, "I've just written an
		  introduction to one of your papers. Read it so I can send
		  it out right away for publication." Dantzig had no idea
		  what he was talking about. He later learned that the
		  "homework" problems had in fact been two famous unsolved
		  problems in statistics.},
  author	= {Dantzig, George B.},
  isbn		= 0691059136,
  keywords	= {linear, programming},
  month		= {August},
  publisher	= {Princeton University Press},
  title		= {Linear Programming and Extensions},
  year		= 1998
}

@Article{	  dasdenn:1997,
  author	= {I. Das and J. E. Dennis},
  date-added	= {2007-12-31 17:27:34 -0800},
  date-modified	= {2007-12-31 17:28:48 -0800},
  journal	= {Struct. Optim.},
  pages		= {63--69},
  title		= {A closer look at drawbacks of minimizing weighted sums of
		  objectives for {Pareto} set generation in multicriteria
		  optimization problems},
  volume	= 14,
  year		= 1997
}

###Article{	  dasdenn:1997,
  author	= {I. Das and J. E. Dennis},
  date-added	= {2007-12-31 17:27:34 -0800},
  date-modified	= {2007-12-31 17:28:48 -0800},
  journal	= {Struct. Optim.},
  pages		= {63--69},
  title		= {A closer look at drawbacks of minimizing weighted sums of
		  objectives for {Pareto} set generation in multicriteria
		  optimization problems},
  volume	= 14,
  year		= 1997
}

###Article{	  dasdenn:1997,
  author	= {I. Das and J. E. Dennis},
  date-added	= {2007-12-31 17:27:34 -0800},
  date-modified	= {2007-12-31 17:28:48 -0800},
  journal	= {Struct. Optim.},
  pages		= {63--69},
  title		= {A closer look at drawbacks of minimizing weighted sums of
		  objectives for {Pareto} set generation in multicriteria
		  optimization problems},
  volume	= 14,
  year		= 1997
}

@Article{	  daubdevofornsina:2009,
  author	= {Daubechies, Ingrid and DeVore, Ronald and Fornasier,
		  Massimo and Güntürk, C. Si̇nan},
  title		= {Iteratively reweighted least squares minimization for
		  sparse recovery},
  journal	= {Communications on Pure and Applied Mathematics},
  volume	= 63,
  number	= 1,
  publisher	= {Wiley Subscription Services, Inc., A Wiley Company},
  issn		= {1097-0312},
  url		= {http://dx.doi.org/10.1002/cpa.20303},
  doi		= {10.1002/cpa.20303},
  pages		= {1--38},
  year		= 2010,
  abstract	= {Abstract Under certain conditions (known as the restricted
		  isometry property, or RIP) on the m × N matrix Φ (where m
		  < N), vectors x ∈ ℝN that are sparse (i.e., have most
		  of their entries equal to 0) can be recovered exactly from
		  y := Φx even though Φ−1(y) is typically an (N −
		  m)—dimensional hyperplane; in addition, x is then equal
		  to the element in Φ−1(y) of minimal 𝓁1-norm. This
		  minimal element can be identified via linear programming
		  algorithms. We study an alternative method of determining
		  x, as the limit of an iteratively reweighted least squares
		  (IRLS) algorithm. The main step of this IRLS finds, for a
		  given weight vector w, the element in Φ−1(y) with
		  smallest 𝓁2(w)-norm. If x(n) is the solution at
		  iteration step n, then the new weight w(n) is defined by
		  w i(n) := [|x i(n)|2 + ε n2]−1/2, i = 1, …, N,
		  for a decreasing sequence of adaptively defined εn; this
		  updated weight is then used to obtain x(n + 1) and the
		  process is repeated. We prove that when Φ satisfies the
		  RIP conditions, the sequence x(n) converges for all y,
		  regardless of whether Φ−1(y) contains a sparse vector.
		  If there is a sparse vector in Φ−1(y), then the limit is
		  this sparse vector, and when x(n) is sufficiently close to
		  the limit, the remaining steps of the algorithm converge
		  exponentially fast (linear convergence in the terminology
		  of numerical optimization). The same algorithm with the
		  “heavier” weight w i(n) = [|x i(n)|2 +
		  ε n2]−1+τ/2, i = 1, …, N, where 0 < τ < 1, can
		  recover sparse solutions as well; more importantly, we show
		  its local convergence is superlinear and approaches a
		  quadratic rate for τ approaching 0. © 2009 Wiley
		  Periodicals, Inc.}
}

###Article{	  daubdevofornsina:2009,
  author	= {Daubechies, Ingrid and DeVore, Ronald and Fornasier,
		  Massimo and Güntürk, C. Si̇nan},
  title		= {Iteratively reweighted least squares minimization for
		  sparse recovery},
  journal	= {Communications on Pure and Applied Mathematics},
  volume	= 63,
  number	= 1,
  publisher	= {Wiley Subscription Services, Inc., A Wiley Company},
  url		= {http://dx.doi.org/10.1002/cpa.20303},
  doi		= {10.1002/cpa.20303},
  pages		= {1--38},
  year		= 2010,
  abstract	= {Abstract Under certain conditions (known as the restricted
		  isometry property, or RIP) on the m × N matrix Φ (where m
		  < N), vectors x ∈ ℝN that are sparse (i.e., have most
		  of their entries equal to 0) can be recovered exactly from
		  y := Φx even though Φ−1(y) is typically an (N −
		  m)—dimensional hyperplane; in addition, x is then equal
		  to the element in Φ−1(y) of minimal �1-norm. This
		  minimal element can be identified via linear programming
		  algorithms. We study an alternative method of determining
		  x, as the limit of an iteratively reweighted least squares
		  (IRLS) algorithm. The main step of this IRLS finds, for a
		  given weight vector w, the element in Φ−1(y) with
		  smallest �2(w)-norm. If x(n) is the solution at iteration
		  step n, then the new weight w(n) is defined by w i(n) :=
		  [|x i(n)|2 + ε n2]−1/2, i = 1, …, N, for a
		  decreasing sequence of adaptively defined εn; this updated
		  weight is then used to obtain x(n + 1) and the process is
		  repeated. We prove that when Φ satisfies the RIP
		  conditions, the sequence x(n) converges for all y,
		  regardless of whether Φ−1(y) contains a sparse vector.
		  If there is a sparse vector in Φ−1(y), then the limit is
		  this sparse vector, and when x(n) is sufficiently close to
		  the limit, the remaining steps of the algorithm converge
		  exponentially fast (linear convergence in the terminology
		  of numerical optimization). The same algorithm with the
		  “heavier” weight w i(n) = [|x i(n)|2 +
		  ε n2]−1+τ/2, i = 1, …, N, where 0 < τ < 1, can
		  recover sparse solutions as well; more importantly, we show
		  its local convergence is superlinear and approaches a
		  quadratic rate for τ approaching 0. © 2009 Wiley
		  Periodicals, Inc.}
}

###Article{	  daubdevofornsina:2009,
  author	= {Daubechies, Ingrid and DeVore, Ronald and Fornasier,
		  Massimo and Güntürk, C. Si̇nan},
  title		= {Iteratively reweighted least squares minimization for
		  sparse recovery},
  journal	= {Communications on Pure and Applied Mathematics},
  volume	= 63,
  number	= 1,
  publisher	= {Wiley Subscription Services, Inc., A Wiley Company},
  url		= {http://dx.doi.org/10.1002/cpa.20303},
  doi		= {10.1002/cpa.20303},
  pages		= {1--38},
  year		= 2010,
  abstract	= {Abstract Under certain conditions (known as the restricted
		  isometry property, or RIP) on the m × N matrix Φ (where m
		  < N), vectors x ∈ ℝN that are sparse (i.e., have most
		  of their entries equal to 0) can be recovered exactly from
		  y := Φx even though Φ−1(y) is typically an (N −
		  m)—dimensional hyperplane; in addition, x is then equal
		  to the element in Φ−1(y) of minimal �1-norm. This
		  minimal element can be identified via linear programming
		  algorithms. We study an alternative method of determining
		  x, as the limit of an iteratively reweighted least squares
		  (IRLS) algorithm. The main step of this IRLS finds, for a
		  given weight vector w, the element in Φ−1(y) with
		  smallest �2(w)-norm. If x(n) is the solution at iteration
		  step n, then the new weight w(n) is defined by w i(n) :=
		  [|x i(n)|2 + ε n2]−1/2, i = 1, …, N, for a
		  decreasing sequence of adaptively defined εn; this updated
		  weight is then used to obtain x(n + 1) and the process is
		  repeated. We prove that when Φ satisfies the RIP
		  conditions, the sequence x(n) converges for all y,
		  regardless of whether Φ−1(y) contains a sparse vector.
		  If there is a sparse vector in Φ−1(y), then the limit is
		  this sparse vector, and when x(n) is sufficiently close to
		  the limit, the remaining steps of the algorithm converge
		  exponentially fast (linear convergence in the terminology
		  of numerical optimization). The same algorithm with the
		  “heavier” weight w i(n) = [|x i(n)|2 +
		  ε n2]−1+τ/2, i = 1, …, N, where 0 < τ < 1, can
		  recover sparse solutions as well; more importantly, we show
		  its local convergence is superlinear and approaches a
		  quadratic rate for τ approaching 0. © 2009 Wiley
		  Periodicals, Inc.}
}

@Article{	  daubfornlori:2007,
  author	= {I. Daubechies and M. Fornasier and I. Loris},
  title		= {Accelereated projected gradient method for linear inverse
		  problems with sparsity constraints},
  journal	= {J. Fourier Anal. Appl.},
  year		= 2007,
  note		= {To appear}
}

###Article{	  daubfornlori:2007,
  author	= {I. Daubechies and M. Fornasier and I. Loris},
  title		= {Accelereated projected gradient method for linear inverse
		  problems with sparsity constraints},
  journal	= {J. Fourier Anal. Appl.},
  year		= 2007,
  note		= {To appear}
}

###Article{	  daubfornlori:2007,
  author	= {I. Daubechies and M. Fornasier and I. Loris},
  title		= {Accelereated projected gradient method for linear inverse
		  problems with sparsity constraints},
  journal	= {J. Fourier Anal. Appl.},
  year		= 2007,
  note		= {To appear}
}

@TechReport{	  davies2008,
  author	= "Davies, M. E. and Gribonval, R.",
  title		= "Restricted isometry constants where $\ell-p$ sparse
		  recovery can fail for $0 < p \le 1$",
  institution	= "IRISA",
  number	= 1899,
  address	= "Rennes",
  month		= "May",
  year		= 2008
}

###TechReport{	  davies2008,
  author	= "Davies, M. E. and Gribonval, R.",
  title		= "Restricted isometry constants where $\ell-p$ sparse
		  recovery can fail for $0 < p \le 1$",
  institution	= "IRISA",
  number	= 1899,
  address	= "Rennes",
  month		= "May",
  year		= 2008
}

###TechReport{	  davies2008,
  author	= "Davies, M. E. and Gribonval, R.",
  title		= "Restricted isometry constants where $\ell-p$ sparse
		  recovery can fail for $0 < p \le 1$",
  institution	= "IRISA",
  number	= 1899,
  address	= "Rennes",
  month		= "May",
  year		= 2008
}

@Article{	  davis2011university,
  title		= {The University of Florida sparse matrix collection},
  author	= {Davis, Timothy A and Hu, Yifan},
  journal	= acmmathsoft,
  volume	= 38,
  number	= 1,
  pages		= 1,
  year		= 2011,
  publisher	= {ACM}
}

###Article{	  davis2011university,
  title		= {The University of Florida sparse matrix collection},
  author	= {Davis, Timothy A and Hu, Yifan},
  journal	= acmmathsoft,
  volume	= 38,
  number	= 1,
  pages		= 1,
  year		= 2011,
  publisher	= {ACM}
}

@Article{	  daw80,
  author	= "Dasarathy, B. and White, L. J.",
  title		= "A maxmin location problem",
  journal	= "Oper. Res.",
  year		= 1980,
  volume	= 28,
  number	= 6,
  page		= {1385--1401}
}

###Article{	  daw80,
  author	= "Dasarathy, B. and White, L. J.",
  title		= "A maxmin location problem",
  journal	= "Oper. Res.",
  year		= 1980,
  volume	= 28,
  number	= 6,
  page		= {1385--1401}
}

###Article{	  daw80,
  author	= "Dasarathy, B. and White, L. J.",
  title		= "A maxmin location problem",
  journal	= "Oper. Res.",
  year		= 1980,
  volume	= 28,
  number	= 6,
  page		= {1385--1401}
}

@Article{	  dax:1992,
  author	= {A. Dax},
  date-modified	= {2007-07-18 14:59:33 -0700},
  doi		= {10.1137/0802029},
  journal	= siamopt,
  keywords	= {lp least norm problems; regularization; optimality
		  conditions; behavior of regularized solutions; duality
		  relations},
  number	= 4,
  pages		= {602-618},
  publisher	= {SIAM},
  title		= {On Regularized Least Norm Problems},
  url		= {http://link.aip.org/link/?SJE/2/602/1},
  volume	= 2,
  year		= 1992
}

###Article{	  dax:1992,
  author	= {A. Dax},
  date-modified	= {2007-07-18 14:59:33 -0700},
  doi		= {10.1137/0802029},
  journal	= siamopt,
  keywords	= {lp least norm problems; regularization; optimality
		  conditions; behavior of regularized solutions; duality
		  relations},
  number	= 4,
  pages		= {602-618},
  publisher	= {SIAM},
  title		= {On Regularized Least Norm Problems},
  url		= {http://link.aip.org/link/?SJE/2/602/1},
  volume	= 2,
  year		= 1992
}

###Article{	  dax:1992,
  author	= {A. Dax},
  date-modified	= {2007-07-18 14:59:33 -0700},
  doi		= {10.1137/0802029},
  journal	= siamopt,
  keywords	= {lp least norm problems; regularization; optimality
		  conditions; behavior of regularized solutions; duality
		  relations},
  number	= 4,
  pages		= {602-618},
  publisher	= {SIAM},
  title		= {On Regularized Least Norm Problems},
  url		= {http://link.aip.org/link/?SJE/2/602/1},
  volume	= 2,
  year		= 1992
}

@Article{	  dbg08,
  author	= "{\noopsort{Asprement}}{D'Aspremont}, A. and Onureena
		  Banerjee and Laurent El Ghaoui",
  title		= "First-order methods for sparse covariance selection",
  journal	= siammatrix,
  year		= 2008,
  volume	= 30,
  number	= 1,
  pages		= "56--66"
}

###Article{	  dbg08,
  author	= "{\noopsort{Asprement}}{D'Aspremont}, A. and Onureena
		  Banerjee and Laurent El Ghaoui",
  title		= "First-order methods for sparse covariance selection",
  journal	= siammatrix,
  year		= 2008,
  volume	= 30,
  number	= 1,
  pages		= "56--66"
}

###Article{	  dbg08,
  author	= "{\noopsort{Asprement}}{D'Aspremont}, A. and Onureena
		  Banerjee and Laurent El Ghaoui",
  title		= "First-order methods for sparse covariance selection",
  journal	= siammatrix,
  year		= 2008,
  volume	= 30,
  number	= 1,
  pages		= "56--66"
}

@Article{	  ddd04,
  author	= {I. Daubechies and M. Defrise and C. De Mol},
  issue		= 11,
  journal	= {Comm. Pure Appl. Math.},
  pages		= {1413--1457},
  title		= {An iterative thresholding algorithm for linear inverse
		  problems with a sparsity constraint},
  volume	= 57,
  year		= 2004
}

###Article{	  ddd04,
  author	= {I. Daubechies and M. Defrise and C. De Mol},
  issue		= 11,
  journal	= {Comm. Pure Appl. Math.},
  pages		= {1413--1457},
  title		= {An iterative thresholding algorithm for linear inverse
		  problems with a sparsity constraint},
  volume	= 57,
  year		= 2004
}

###Article{	  ddd04,
  author	= {I. Daubechies and M. Defrise and C. De Mol},
  issue		= 11,
  journal	= {Comm. Pure Appl. Math.},
  pages		= {1413--1457},
  title		= {An iterative thresholding algorithm for linear inverse
		  problems with a sparsity constraint},
  volume	= 57,
  year		= 2004
}

@Article{	  de2014convex,
  title		= {Convex proximal bundle methods in depth: a unified
		  analysis for inexact oracles},
  author	= {de Oliveira, W and Sagastiz{\'a}bal, C and Lemar{\'e}chal,
		  Claude},
  journal	= {Mathematical Programming},
  volume	= {148},
  number	= {1-2},
  pages		= {241--277},
  year		= {2014},
  publisher	= {Springer}
}

###Article{	  de2014convex,
  title		= {Convex proximal bundle methods in depth: a unified
		  analysis for inexact oracles},
  author	= {de Oliveira, W and Sagastiz{\'a}bal, C and Lemar{\'e}chal,
		  Claude},
  journal	= {Mathematical Programming},
  volume	= {148},
  number	= {1-2},
  pages		= {241--277},
  year		= {2014},
  publisher	= {Springer}
}

@Article{	  debr:1952,
  author	= {Gerard Debreu},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Econometrica},
  month		= {April},
  number	= 2,
  pages		= {295--300},
  title		= {Definite and semidefinite quadratic forms},
  volume	= 20,
  year		= 1952
}

###Article{	  debr:1952,
  author	= {Gerard Debreu},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Econometrica},
  month		= {April},
  number	= 2,
  pages		= {295--300},
  title		= {Definite and semidefinite quadratic forms},
  volume	= 20,
  year		= 1952
}

###Article{	  debr:1952,
  author	= {Gerard Debreu},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Econometrica},
  month		= {April},
  number	= 2,
  pages		= {295--300},
  title		= {Definite and semidefinite quadratic forms},
  volume	= 20,
  year		= 1952
}

@Article{	  delgil:05,
  author	= {F. Delbos and J. Gilbert},
  title		= {Global linear convergence of an augmented {L}agrangian
		  algorithm for solving convex quadratic optimization
		  problems},
  journal	= {J. Convex Anal.},
  volume	= 12,
  pages		= {45--69},
  year		= 2005
}

###Article{	  delgil:05,
  author	= {F. Delbos and J. Gilbert},
  title		= {Global linear convergence of an augmented {L}agrangian
		  algorithm for solving convex quadratic optimization
		  problems},
  journal	= {J. Convex Anal.},
  volume	= 12,
  pages		= {45--69},
  year		= 2005
}

###Article{	  delgil:05,
  author	= {F. Delbos and J. Gilbert},
  title		= {Global linear convergence of an augmented {L}agrangian
		  algorithm for solving convex quadratic optimization
		  problems},
  journal	= {J. Convex Anal.},
  volume	= 12,
  pages		= {45--69},
  year		= 2005
}

@Article{	  dembeisestei:1982,
  author	= {Ron S. Dembo and Stanley C. Eisenstat and Trond Steihaug},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamnumanal,
  number	= 2,
  pages		= {400--408},
  title		= {Inexact {N}ewton methods},
  volume	= 19,
  year		= 1982
}

###Article{	  dembeisestei:1982,
  author	= {Ron S. Dembo and Stanley C. Eisenstat and Trond Steihaug},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamnumanal,
  number	= 2,
  pages		= {400--408},
  title		= {Inexact {N}ewton methods},
  volume	= 19,
  year		= 1982
}

###Article{	  dembeisestei:1982,
  author	= {Ron S. Dembo and Stanley C. Eisenstat and Trond Steihaug},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamnumanal,
  number	= 2,
  pages		= {400--408},
  title		= {Inexact {N}ewton methods},
  volume	= 19,
  year		= 1982
}

@Article{	  dennel-awill:1999,
  author	= {J. E. Dennis and Majmoud {El-Alem} and Karen Williamson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamopt,
  number	= 2,
  pages		= {291-315},
  title		= {A trust-region approach to nonlinear systems of equalities
		  and inequalities},
  volume	= 9,
  year		= 1999
}

###Article{	  dennel-awill:1999,
  author	= {J. E. Dennis and Majmoud {El-Alem} and Karen Williamson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamopt,
  number	= 2,
  pages		= {291-315},
  title		= {A trust-region approach to nonlinear systems of equalities
		  and inequalities},
  volume	= 9,
  year		= 1999
}

###Article{	  dennel-awill:1999,
  author	= {J. E. Dennis and Majmoud {El-Alem} and Karen Williamson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamopt,
  number	= 2,
  pages		= {291-315},
  title		= {A trust-region approach to nonlinear systems of equalities
		  and inequalities},
  volume	= 9,
  year		= 1999
}

@Book{		  dennschn:1996,
  address	= {Philadelphia},
  author	= {J. E. Dennis and R. B. Schnabel},
  date-modified	= {2007-07-18 14:59:33 -0700},
  note		= {Originally published: Prentice-Hall, New Jersey, 1983},
  publisher	= siampub,
  series	= {Classics in Applied Mathematics},
  title		= {Numerical Methods for Unconstrained Optimization and
		  Nonlinear Equations},
  year		= 1996
}

###Book{	  dennschn:1996,
  address	= {Philadelphia},
  author	= {J. E. Dennis and R. B. Schnabel},
  date-modified	= {2007-07-18 14:59:33 -0700},
  note		= {Originally published: Prentice-Hall, New Jersey, 1983},
  publisher	= siampub,
  series	= {Classics in Applied Mathematics},
  title		= {Numerical Methods for Unconstrained Optimization and
		  Nonlinear Equations},
  year		= 1996
}

###Book{	  dennschn:1996,
  address	= {Philadelphia},
  author	= {J. E. Dennis and R. B. Schnabel},
  date-modified	= {2007-07-18 14:59:33 -0700},
  note		= {Originally published: Prentice-Hall, New Jersey, 1983},
  publisher	= siampub,
  series	= {Classics in Applied Mathematics},
  title		= {Numerical Methods for Unconstrained Optimization and
		  Nonlinear Equations},
  year		= 1996
}

@Article{	  det06,
  author	= {D. L. Donoho and M. Elad and V. Temlyakov},
  journal	= ieeetransinfo,
  number	= 1,
  pages		= {6-18},
  title		= {Stable Recovery of Sparse Overcomplete Representations in
		  the Presence of Noise},
  volume	= 52,
  year		= 2006
}

###Article{	  det06,
  author	= {D. L. Donoho and M. Elad and V. Temlyakov},
  journal	= ieeetransinfo,
  number	= 1,
  pages		= {6-18},
  title		= {Stable Recovery of Sparse Overcomplete Representations in
		  the Presence of Noise},
  volume	= 52,
  year		= 2006
}

###Article{	  det06,
  author	= {D. L. Donoho and M. Elad and V. Temlyakov},
  journal	= ieeetransinfo,
  number	= 1,
  pages		= {6-18},
  title		= {Stable Recovery of Sparse Overcomplete Representations in
		  the Presence of Noise},
  volume	= 52,
  year		= 2006
}

@Article{	  devolder2014first,
  title		= {First-order methods of smooth convex optimization with
		  inexact oracle},
  author	= {Devolder, Olivier and Glineur, Fran{\c{c}}ois and
		  Nesterov, Yurii},
  journal	= {Mathematical Programming},
  volume	= {146},
  number	= {1-2},
  pages		= {37--75},
  year		= {2014},
  publisher	= {Springer}
}

@Book{		  devrgyorlugo:1996,
  address	= {New York},
  author	= {L. Devroye and L. Gyorfi and G. Lugosi},
  date-modified	= {2007-07-18 14:59:31 -0700},
  publisher	= springer,
  title		= {A Probabilistic Theory of Pattern Recognition},
  year		= 1996
}

###Book{	  devrgyorlugo:1996,
  address	= {New York},
  author	= {L. Devroye and L. Gyorfi and G. Lugosi},
  date-modified	= {2007-07-18 14:59:31 -0700},
  publisher	= springer,
  title		= {A Probabilistic Theory of Pattern Recognition},
  year		= 1996
}

###Book{	  devrgyorlugo:1996,
  address	= {New York},
  author	= {L. Devroye and L. Gyorfi and G. Lugosi},
  date-modified	= {2007-07-18 14:59:31 -0700},
  publisher	= springer,
  title		= {A Probabilistic Theory of Pattern Recognition},
  year		= 1996
}

@TechReport{	  dinigomesant:1998,
  abstract	= {In this work, we focus our attention on the quadratic
		  subproblem of trust-region algorithms for large-scale
		  bound-constrained minimization. An approach that combines a
		  mild active set strategy with gradient projection
		  techniques is employed in the solution of large-scale
		  bound-constrained quadratic problems. To fill in some gaps
		  that have appeared in previous work, we propose, test and
		  analyze heuristics which dynamically choose the parameters
		  in charge of the decision of leaving or not the current
		  face of the feasible set. The numerical analysis is based
		  on problems from CUTE collection and randomly generated
		  convex problems with controlled conditioning and
		  degeneracy. The practical consequences of an appropriate
		  decision of such parameters are shown to be crucial,
		  particularly when dual degenerate and ill-conditioned
		  problems are solved.},
  address	= {Campinas, Brasil},
  author	= {M. A. Diniz{-}Ehrhardt and M. A. Gomes{-}Ruggiero and S.
		  A. Santos},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Department of Applied Mathematics, IMECC-UNICAMP},
  number	= {52/98},
  title		= {Numerical analysis of leaving-face parameters in
		  bound-constrained quadratic minimization},
  year		= 1998
}

###TechReport{	  dinigomesant:1998,
  abstract	= {In this work, we focus our attention on the quadratic
		  subproblem of trust-region algorithms for large-scale
		  bound-constrained minimization. An approach that combines a
		  mild active set strategy with gradient projection
		  techniques is employed in the solution of large-scale
		  bound-constrained quadratic problems. To fill in some gaps
		  that have appeared in previous work, we propose, test and
		  analyze heuristics which dynamically choose the parameters
		  in charge of the decision of leaving or not the current
		  face of the feasible set. The numerical analysis is based
		  on problems from CUTE collection and randomly generated
		  convex problems with controlled conditioning and
		  degeneracy. The practical consequences of an appropriate
		  decision of such parameters are shown to be crucial,
		  particularly when dual degenerate and ill-conditioned
		  problems are solved.},
  address	= {Campinas, Brasil},
  author	= {M. A. Diniz{-}Ehrhardt and M. A. Gomes{-}Ruggiero and S.
		  A. Santos},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Department of Applied Mathematics, IMECC-UNICAMP},
  number	= {52/98},
  title		= {Numerical analysis of leaving-face parameters in
		  bound-constrained quadratic minimization},
  year		= 1998
}

###TechReport{	  dinigomesant:1998,
  abstract	= {In this work, we focus our attention on the quadratic
		  subproblem of trust-region algorithms for large-scale
		  bound-constrained minimization. An approach that combines a
		  mild active set strategy with gradient projection
		  techniques is employed in the solution of large-scale
		  bound-constrained quadratic problems. To fill in some gaps
		  that have appeared in previous work, we propose, test and
		  analyze heuristics which dynamically choose the parameters
		  in charge of the decision of leaving or not the current
		  face of the feasible set. The numerical analysis is based
		  on problems from CUTE collection and randomly generated
		  convex problems with controlled conditioning and
		  degeneracy. The practical consequences of an appropriate
		  decision of such parameters are shown to be crucial,
		  particularly when dual degenerate and ill-conditioned
		  problems are solved.},
  address	= {Campinas, Brasil},
  author	= {M. A. Diniz{-}Ehrhardt and M. A. Gomes{-}Ruggiero and S.
		  A. Santos},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Department of Applied Mathematics, IMECC-UNICAMP},
  number	= {52/98},
  title		= {Numerical analysis of leaving-face parameters in
		  bound-constrained quadratic minimization},
  year		= 1998
}

@TechReport{	  dkqw08,
  author	= "Ding, Y. and Krislock, N. and Qian, J. and Wolkowicz, H.",
  title		= "Sensor Network Localization, Euclidean Distance Matrix
		  Completions, and Graph Realization",
  institution	= "Department of Combinatorics and Optimization, University
		  of Waterloo",
  address	= "Waterloo",
  month		= "February",
  year		= 2008
}

###TechReport{	  dkqw08,
  author	= "Ding, Y. and Krislock, N. and Qian, J. and Wolkowicz, H.",
  title		= "Sensor Network Localization, Euclidean Distance Matrix
		  Completions, and Graph Realization",
  institution	= "Department of Combinatorics and Optimization, University
		  of Waterloo",
  address	= "Waterloo",
  month		= "February",
  year		= 2008
}

###TechReport{	  dkqw08,
  author	= "Ding, Y. and Krislock, N. and Qian, J. and Wolkowicz, H.",
  title		= "Sensor Network Localization, Euclidean Distance Matrix
		  Completions, and Graph Realization",
  institution	= "Department of Combinatorics and Optimization, University
		  of Waterloo",
  address	= "Waterloo",
  month		= "February",
  year		= 2008
}

@Article{	  doe03,
  abstract	= {Given a dictionary D = {dk} of vectors dk, we seek to
		  represent a signal S as a linear combination S = {sum}k
		  {gamma}(k)dk, with scalar coefficients {gamma}(k). In
		  particular, we aim for the sparsest representation
		  possible. In general, this requires a combinatorial
		  optimization process. Previous work considered the special
		  case where D is an overcomplete system consisting of
		  exactly two orthobases and has shown that, under a
		  condition of mutual incoherence of the two bases, and
		  assuming that S has a sufficiently sparse representation,
		  this representation is unique and can be found by solving a
		  convex optimization problem: specifically, minimizing the
		  {ell}1 norm of the coefficients{gamma} . In this article,
		  we obtain parallel results in a more general setting, where
		  the dictionary D can arise from two or several bases,
		  frames, or even less structured systems. We sketch three
		  applications: separating linear features from planar ones
		  in 3D data, noncooperative multiuser encoding, and
		  identification of over-complete independent component
		  models. },
  author	= {D. L. Donoho and M. Elad},
  doi		= {10.1073/pnas.0437847100},
  eprint	= {http://www.pnas.org/cgi/reprint/100/5/2197.pdf},
  journal	= procnas,
  number	= 5,
  pages		= {2197-2202},
  title		= {Optimally sparse representation in general (nonorthogonal)
		  dictionaries via $\ell_1$ minimization},
  url		= {http://www.pnas.org/cgi/content/abstract/100/5/2197},
  volume	= 100,
  year		= 2003
}

###Article{	  doe03,
  abstract	= {Given a dictionary D = {dk} of vectors dk, we seek to
		  represent a signal S as a linear combination S = {sum}k
		  {gamma}(k)dk, with scalar coefficients {gamma}(k). In
		  particular, we aim for the sparsest representation
		  possible. In general, this requires a combinatorial
		  optimization process. Previous work considered the special
		  case where D is an overcomplete system consisting of
		  exactly two orthobases and has shown that, under a
		  condition of mutual incoherence of the two bases, and
		  assuming that S has a sufficiently sparse representation,
		  this representation is unique and can be found by solving a
		  convex optimization problem: specifically, minimizing the
		  {ell}1 norm of the coefficients{gamma} . In this article,
		  we obtain parallel results in a more general setting, where
		  the dictionary D can arise from two or several bases,
		  frames, or even less structured systems. We sketch three
		  applications: separating linear features from planar ones
		  in 3D data, noncooperative multiuser encoding, and
		  identification of over-complete independent component
		  models. },
  author	= {D. L. Donoho and M. Elad},
  doi		= {10.1073/pnas.0437847100},
  eprint	= {http://www.pnas.org/cgi/reprint/100/5/2197.pdf},
  journal	= procnas,
  number	= 5,
  pages		= {2197-2202},
  title		= {Optimally sparse representation in general (nonorthogonal)
		  dictionaries via $\ell_1$ minimization},
  url		= {http://www.pnas.org/cgi/content/abstract/100/5/2197},
  volume	= 100,
  year		= 2003
}

###Article{	  doe03,
  abstract	= {Given a dictionary D = {dk} of vectors dk, we seek to
		  represent a signal S as a linear combination S = {sum}k
		  {gamma}(k)dk, with scalar coefficients {gamma}(k). In
		  particular, we aim for the sparsest representation
		  possible. In general, this requires a combinatorial
		  optimization process. Previous work considered the special
		  case where D is an overcomplete system consisting of
		  exactly two orthobases and has shown that, under a
		  condition of mutual incoherence of the two bases, and
		  assuming that S has a sufficiently sparse representation,
		  this representation is unique and can be found by solving a
		  convex optimization problem: specifically, minimizing the
		  {ell}1 norm of the coefficients{gamma} . In this article,
		  we obtain parallel results in a more general setting, where
		  the dictionary D can arise from two or several bases,
		  frames, or even less structured systems. We sketch three
		  applications: separating linear features from planar ones
		  in 3D data, noncooperative multiuser encoding, and
		  identification of over-complete independent component
		  models. },
  author	= {D. L. Donoho and M. Elad},
  doi		= {10.1073/pnas.0437847100},
  eprint	= {http://www.pnas.org/cgi/reprint/100/5/2197.pdf},
  journal	= procnas,
  number	= 5,
  pages		= {2197-2202},
  title		= {Optimally sparse representation in general (nonorthogonal)
		  dictionaries via $\ell_1$ minimization},
  url		= {http://www.pnas.org/cgi/content/abstract/100/5/2197},
  volume	= 100,
  year		= 2003
}

@Article{	  doh01,
  abstract	= {Suppose a discrete-time signal $S(t)$, $0 \leq t < N$, is
		  a superposition of atoms taken from a combined
		  time-frequency dictionary made of spike sequences
		  $1_{t=\tau}$ and sinusoids $\exp\{2\pi iw t/N\}/\sqrt{N}$.
		  Can one recover, from knowledge of $S$ alone, the precise
		  collection of atoms going to make up $S$? Because every
		  discrete-time signal can be represented as a superposition
		  of spikes alone, or as a superposition of sinusoids alone,
		  there is no unique way of writing S as a sum of spikes and
		  sinusoids in general. We prove that if $S$ is representable
		  as a highly sparse superposition of atoms from this
		  time-frequency dictionary, then there is only one such
		  highly sparse representation of $S$, and it can be obtained
		  by solving the convex optimization problem of minimizing
		  the $\ell^1$ norm of the coefficients among all
		  decompositions. Here ``highly sparse'' means that $N_t+N_w
		  < \sqrt{N}/2$ where $N_t$ is the number of time atoms,
		  $N_w$ is the number of frequency atoms, and $N$ is the
		  length of the discrete-time signal. Underlying this result
		  is a general $\ell^1$ uncertainty principle which says that
		  if two bases are mutually incoherent, no nonzero signal can
		  have a sparse representation in both bases simultaneously.
		  For the above setting, the bases are sinusoids and spikes,
		  and mutual incoherence is measured in terms of the largest
		  inner product between different basis elements. The
		  uncertainty principle holds for a variety of interesting
		  basis pairs, not just sinusoids and spikes. The results
		  have idealized applications to band-limited approximation
		  with gross errors, to error-correcting encryption, and to
		  separation of uncoordinated sources. Related phenomena hold
		  for functions of a real variable, with basis pairs such as
		  sinusoids and wavelets, and for functions of two variables,
		  with basis pairs such as wavelets and ridgelets. In these
		  settings, if a function f is representable by a
		  sufficiently sparse superposition of terms taken from both
		  bases, then there is only one such sparse representation;
		  it may be obtained by minimum $\ell^1$ norm atomic
		  decomposition. The condition ``sufficiently sparse''
		  becomes a multiscale condition; for example, that the
		  number of wavelets at level $j$ plus the number of
		  sinusoids in the $j$th dyadic frequency band are together
		  less than a constant times $2^{j/2}$. },
  author	= {David L. Donoho and Xiaoming Huo},
  journal	= ieeetransinfo,
  keywords	= {Basis pursuit, combinatorial optimization, convex
		  optimization, error-correcting encryption, harmonic
		  analysis, Logan's phenomenon, matching pursuit,
		  multiple-basis signal representation, overcomplete
		  representation, ridgelet analysis, uncertainty principle,
		  wavelet analysis},
  month		= {November},
  number	= 7,
  pages		= {2845--2862},
  title		= {Uncertainty Principles and Ideal Atomic Decomposition},
  url		= {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=959265},
  volume	= 47,
  year		= 2001
}

###Article{	  doh01,
  abstract	= {Suppose a discrete-time signal $S(t)$, $0 \leq t < N$, is
		  a superposition of atoms taken from a combined
		  time-frequency dictionary made of spike sequences
		  $1_{t=\tau}$ and sinusoids $\exp\{2\pi iw t/N\}/\sqrt{N}$.
		  Can one recover, from knowledge of $S$ alone, the precise
		  collection of atoms going to make up $S$? Because every
		  discrete-time signal can be represented as a superposition
		  of spikes alone, or as a superposition of sinusoids alone,
		  there is no unique way of writing S as a sum of spikes and
		  sinusoids in general. We prove that if $S$ is representable
		  as a highly sparse superposition of atoms from this
		  time-frequency dictionary, then there is only one such
		  highly sparse representation of $S$, and it can be obtained
		  by solving the convex optimization problem of minimizing
		  the $\ell^1$ norm of the coefficients among all
		  decompositions. Here ``highly sparse'' means that $N_t+N_w
		  < \sqrt{N}/2$ where $N_t$ is the number of time atoms,
		  $N_w$ is the number of frequency atoms, and $N$ is the
		  length of the discrete-time signal. Underlying this result
		  is a general $\ell^1$ uncertainty principle which says that
		  if two bases are mutually incoherent, no nonzero signal can
		  have a sparse representation in both bases simultaneously.
		  For the above setting, the bases are sinusoids and spikes,
		  and mutual incoherence is measured in terms of the largest
		  inner product between different basis elements. The
		  uncertainty principle holds for a variety of interesting
		  basis pairs, not just sinusoids and spikes. The results
		  have idealized applications to band-limited approximation
		  with gross errors, to error-correcting encryption, and to
		  separation of uncoordinated sources. Related phenomena hold
		  for functions of a real variable, with basis pairs such as
		  sinusoids and wavelets, and for functions of two variables,
		  with basis pairs such as wavelets and ridgelets. In these
		  settings, if a function f is representable by a
		  sufficiently sparse superposition of terms taken from both
		  bases, then there is only one such sparse representation;
		  it may be obtained by minimum $\ell^1$ norm atomic
		  decomposition. The condition ``sufficiently sparse''
		  becomes a multiscale condition; for example, that the
		  number of wavelets at level $j$ plus the number of
		  sinusoids in the $j$th dyadic frequency band are together
		  less than a constant times $2^{j/2}$. },
  author	= {David L. Donoho and Xiaoming Huo},
  journal	= ieeetransinfo,
  keywords	= {Basis pursuit, combinatorial optimization, convex
		  optimization, error-correcting encryption, harmonic
		  analysis, Logan's phenomenon, matching pursuit,
		  multiple-basis signal representation, overcomplete
		  representation, ridgelet analysis, uncertainty principle,
		  wavelet analysis},
  month		= {November},
  number	= 7,
  pages		= {2845--2862},
  title		= {Uncertainty Principles and Ideal Atomic Decomposition},
  url		= {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=959265},
  volume	= 47,
  year		= 2001
}

###Article{	  doh01,
  abstract	= {Suppose a discrete-time signal $S(t)$, $0 \leq t < N$, is
		  a superposition of atoms taken from a combined
		  time-frequency dictionary made of spike sequences
		  $1_{t=\tau}$ and sinusoids $\exp\{2\pi iw t/N\}/\sqrt{N}$.
		  Can one recover, from knowledge of $S$ alone, the precise
		  collection of atoms going to make up $S$? Because every
		  discrete-time signal can be represented as a superposition
		  of spikes alone, or as a superposition of sinusoids alone,
		  there is no unique way of writing S as a sum of spikes and
		  sinusoids in general. We prove that if $S$ is representable
		  as a highly sparse superposition of atoms from this
		  time-frequency dictionary, then there is only one such
		  highly sparse representation of $S$, and it can be obtained
		  by solving the convex optimization problem of minimizing
		  the $\ell^1$ norm of the coefficients among all
		  decompositions. Here ``highly sparse'' means that $N_t+N_w
		  < \sqrt{N}/2$ where $N_t$ is the number of time atoms,
		  $N_w$ is the number of frequency atoms, and $N$ is the
		  length of the discrete-time signal. Underlying this result
		  is a general $\ell^1$ uncertainty principle which says that
		  if two bases are mutually incoherent, no nonzero signal can
		  have a sparse representation in both bases simultaneously.
		  For the above setting, the bases are sinusoids and spikes,
		  and mutual incoherence is measured in terms of the largest
		  inner product between different basis elements. The
		  uncertainty principle holds for a variety of interesting
		  basis pairs, not just sinusoids and spikes. The results
		  have idealized applications to band-limited approximation
		  with gross errors, to error-correcting encryption, and to
		  separation of uncoordinated sources. Related phenomena hold
		  for functions of a real variable, with basis pairs such as
		  sinusoids and wavelets, and for functions of two variables,
		  with basis pairs such as wavelets and ridgelets. In these
		  settings, if a function f is representable by a
		  sufficiently sparse superposition of terms taken from both
		  bases, then there is only one such sparse representation;
		  it may be obtained by minimum $\ell^1$ norm atomic
		  decomposition. The condition ``sufficiently sparse''
		  becomes a multiscale condition; for example, that the
		  number of wavelets at level $j$ plus the number of
		  sinusoids in the $j$th dyadic frequency band are together
		  less than a constant times $2^{j/2}$. },
  author	= {David L. Donoho and Xiaoming Huo},
  journal	= ieeetransinfo,
  keywords	= {Basis pursuit, combinatorial optimization, convex
		  optimization, error-correcting encryption, harmonic
		  analysis, Logan's phenomenon, matching pursuit,
		  multiple-basis signal representation, overcomplete
		  representation, ridgelet analysis, uncertainty principle,
		  wavelet analysis},
  month		= {November},
  number	= 7,
  pages		= {2845--2862},
  title		= {Uncertainty Principles and Ideal Atomic Decomposition},
  url		= {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=959265},
  volume	= 47,
  year		= 2001
}

@Article{	  doi:10.1137/0329022,
  author	= {Osman G\"uler},
  title		= {On the Convergence of the Proximal Point Algorithm for
		  Convex Minimization},
  journal	= {SIAM Journal on Control and Optimization},
  volume	= 29,
  number	= 2,
  pages		= {403-419},
  year		= 1991,
  doi		= {10.1137/0329022},
  url		= { http://dx.doi.org/10.1137/0329022 },
  eprint	= { http://dx.doi.org/10.1137/0329022 }
}

###Article{	  doi:10.1137/0329022,
  author	= {Osman G\"uler},
  title		= {On the Convergence of the Proximal Point Algorithm for
		  Convex Minimization},
  journal	= {SIAM Journal on Control and Optimization},
  volume	= 29,
  number	= 2,
  pages		= {403-419},
  year		= 1991,
  doi		= {10.1137/0329022},
  url		= { http://dx.doi.org/10.1137/0329022 },
  eprint	= { http://dx.doi.org/10.1137/0329022 }
}

@Article{	  doi:10.1137/040612592,
  author	= {Erdougan, E. and Iyengar, G.},
  title		= {An Active Set Method for Single-Cone Second-Order Cone
		  Programs},
  journal	= siamopt,
  volume	= 17,
  number	= 2,
  pages		= {459-484},
  year		= 2006,
  doi		= {10.1137/040612592},
  url		= { http://dx.doi.org/10.1137/040612592 },
  eprint	= { http://dx.doi.org/10.1137/040612592 }
}

###Article{	  doi:10.1137/040612592,
  author	= {Erdougan, E. and Iyengar, G.},
  title		= {An Active Set Method for Single-Cone Second-Order Cone
		  Programs},
  journal	= siamopt,
  volume	= 17,
  number	= 2,
  pages		= {459-484},
  year		= 2006,
  doi		= {10.1137/040612592},
  url		= { http://dx.doi.org/10.1137/040612592 },
  eprint	= { http://dx.doi.org/10.1137/040612592 }
}

@Article{	  doi:10.1137/0802032,
  author	= {Osman G\"uler},
  title		= {New Proximal Point Algorithms for Convex Minimization},
  journal	= {SIAM Journal on Optimization},
  volume	= 2,
  number	= 4,
  pages		= {649-664},
  year		= 1992,
  doi		= {10.1137/0802032},
  url		= { http://dx.doi.org/10.1137/0802032 },
  eprint	= { http://dx.doi.org/10.1137/0802032 }
}

###Article{	  doi:10.1137/0802032,
  author	= {Osman G\"uler},
  title		= {New Proximal Point Algorithms for Convex Minimization},
  journal	= {SIAM Journal on Optimization},
  volume	= 2,
  number	= 4,
  pages		= {649-664},
  year		= 1992,
  doi		= {10.1137/0802032},
  url		= { http://dx.doi.org/10.1137/0802032 },
  eprint	= { http://dx.doi.org/10.1137/0802032 }
}

@Article{	  doi:10.1137/s1052623403428208,
  author	= {Zhang, H. and Hager, W.},
  title		= {A Nonmonotone Line Search Technique and Its Application to
		  Unconstrained Optimization},
  journal	= siamopt,
  volume	= 14,
  number	= 4,
  pages		= {1043-1056},
  year		= 2004,
  doi		= {10.1137/S1052623403428208}
}

###Article{	  doi:10.1137/s1052623403428208,
  author	= {Zhang, H. and Hager, W.},
  title		= {A Nonmonotone Line Search Technique and Its Application to
		  Unconstrained Optimization},
  journal	= siamopt,
  volume	= 14,
  number	= 4,
  pages		= {1043-1056},
  year		= 2004,
  doi		= {10.1137/S1052623403428208}
}

@Article{	  doj94,
  author	= {D. L. Donoho and I. M. Johnstone},
  journal	= {Biometrika},
  number	= 3,
  pages		= {425--455},
  title		= {Ideal spatial adaptation by wavelet shrinkage},
  url		= {http://citeseer.ist.psu.edu/donoho93ideal.html},
  volume	= 81,
  year		= 1994
}

###Article{	  doj94,
  author	= {D. L. Donoho and I. M. Johnstone},
  journal	= {Biometrika},
  number	= 3,
  pages		= {425--455},
  title		= {Ideal spatial adaptation by wavelet shrinkage},
  url		= {http://citeseer.ist.psu.edu/donoho93ideal.html},
  volume	= 81,
  year		= 1994
}

###Article{	  doj94,
  author	= {D. L. Donoho and I. M. Johnstone},
  journal	= {Biometrika},
  number	= 3,
  pages		= {425--455},
  title		= {Ideal spatial adaptation by wavelet shrinkage},
  url		= {http://citeseer.ist.psu.edu/donoho93ideal.html},
  volume	= 81,
  year		= 1994
}

@Article{	  doj95,
  author	= "Donoho, D. L. and Johnstone, I. M.",
  title		= "Adapting to unknown smoothness via wavelet shrinkage",
  journal	= "Journal of the American Statistical Association",
  year		= 1995,
  volume	= 90,
  number	= 432,
  page		= {1200--1224}
}

###Article{	  doj95,
  author	= "Donoho, D. L. and Johnstone, I. M.",
  title		= "Adapting to unknown smoothness via wavelet shrinkage",
  journal	= "Journal of the American Statistical Association",
  year		= 1995,
  volume	= 90,
  number	= 432,
  page		= {1200--1224}
}

###Article{	  doj95,
  author	= "Donoho, D. L. and Johnstone, I. M.",
  title		= "Adapting to unknown smoothness via wavelet shrinkage",
  journal	= "Journal of the American Statistical Association",
  year		= 1995,
  volume	= 90,
  number	= 432,
  page		= {1200--1224}
}

@Article{	  dolafourmoremuns:2002,
  author	= {Elizabeth D. Dolan and Robert Fourer and Jorge J. Mor\'{e}
		  and Todd S. Munson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {{SIAM} {N}ews},
  month		= {July/August},
  number	= 6,
  title		= {Optimization on the {NEOS} server},
  volume	= 35,
  year		= 2002
}

###Article{	  dolafourmoremuns:2002,
  author	= {Elizabeth D. Dolan and Robert Fourer and Jorge J. Mor\'{e}
		  and Todd S. Munson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {{SIAM} {N}ews},
  month		= {July/August},
  number	= 6,
  title		= {Optimization on the {NEOS} server},
  volume	= 35,
  year		= 2002
}

###Article{	  dolafourmoremuns:2002,
  author	= {Elizabeth D. Dolan and Robert Fourer and Jorge J. Mor\'{e}
		  and Todd S. Munson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {{SIAM} {N}ews},
  month		= {July/August},
  number	= 6,
  title		= {Optimization on the {NEOS} server},
  volume	= 35,
  year		= 2002
}

@TechReport{	  dolamore:2000,
  address	= {Argonne, IL},
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Mathematics and Computer Science Division, Argonne
		  National Laboratory},
  note		= {Revised January 2001},
  number	= {ANL/MCS-246},
  title		= {Benchmarking Optimization Software with {COPS}},
  year		= 2000
}

###TechReport{	  dolamore:2000,
  address	= {Argonne, IL},
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Mathematics and Computer Science Division, Argonne
		  National Laboratory},
  note		= {Revised January 2001},
  number	= {ANL/MCS-246},
  title		= {Benchmarking Optimization Software with {COPS}},
  year		= 2000
}

###TechReport{	  dolamore:2000,
  address	= {Argonne, IL},
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Mathematics and Computer Science Division, Argonne
		  National Laboratory},
  note		= {Revised January 2001},
  number	= {ANL/MCS-246},
  title		= {Benchmarking Optimization Software with {COPS}},
  year		= 2000
}

@TechReport{	  dolamore:2001,
  address	= {Argonne, IL},
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  institution	= {Mathematics and Computer Science Division, Argonne
		  National Laboratory},
  number	= {ANL/MCS-P861-1200},
  title		= {Benchmarking Optimization Software with Performance
		  Profiles},
  year		= 2001
}

###TechReport{	  dolamore:2001,
  address	= {Argonne, IL},
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  institution	= {Mathematics and Computer Science Division, Argonne
		  National Laboratory},
  number	= {ANL/MCS-P861-1200},
  title		= {Benchmarking Optimization Software with Performance
		  Profiles},
  year		= 2001
}

###TechReport{	  dolamore:2001,
  address	= {Argonne, IL},
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  institution	= {Mathematics and Computer Science Division, Argonne
		  National Laboratory},
  number	= {ANL/MCS-P861-1200},
  title		= {Benchmarking Optimization Software with Performance
		  Profiles},
  year		= 2001
}

@Article{	  dolamore:2002,
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  journal	= mathprog,
  number	= 2,
  pages		= {201-213},
  title		= {Benchmarking Optimization Software with Performance
		  Profiles},
  volume	= 91,
  year		= 2002
}

###Article{	  dolamore:2002,
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  journal	= mathprog,
  number	= 2,
  pages		= {201-213},
  title		= {Benchmarking Optimization Software with Performance
		  Profiles},
  volume	= 91,
  year		= 2002
}

###Article{	  dolamore:2002,
  author	= {Elizabeth D. Dolan and Jorge J. Mor\'{e}},
  journal	= mathprog,
  number	= 2,
  pages		= {201-213},
  title		= {Benchmarking Optimization Software with Performance
		  Profiles},
  volume	= 91,
  year		= 2002
}

@Article{	  dollgoulschiwath:2006,
  abstract	= {We consider conjugate-gradient like methods for solving
		  block symmetric indefinite linear systems that arise from
		  saddle-point problems or, in particular, regularizations
		  thereof. Such methods require preconditioners that preserve
		  certain sub-blocks from the original systems but allow
		  considerable flexibility for the remaining blocks. We
		  construct a number of families of implicit factorizations
		  that are capable of reproducing the required sub-blocks and
		  (some) of the remainder. These generalize known implicit
		  factorizations for the unregularized case. Improved
		  eigenvalue clustering is possible if additionally some of
		  the noncrucial blocks are reproduced. Numerical experiments
		  confirm that these implicit-factorization preconditioners
		  can be very effective in practice. },
  author	= {H. S. Dollar and N. Gould and W. Schilders and A. J.
		  Wathen},
  doi		= {10.1137/05063427X},
  journal	= {{SIAM} J. Math. Anal.},
  keywords	= {regularized saddle-point systems; implicit-factorization
		  preconditioners},
  number	= 1,
  pages		= {170-189},
  publisher	= {SIAM},
  title		= {Implicit-Factorization Preconditioning and Iterative
		  Solvers for Regularized Saddle-Point Systems},
  url		= {http://link.aip.org/link/?SML/28/170/1},
  volume	= 28,
  year		= 2006
}

###Article{	  dollgoulschiwath:2006,
  abstract	= {We consider conjugate-gradient like methods for solving
		  block symmetric indefinite linear systems that arise from
		  saddle-point problems or, in particular, regularizations
		  thereof. Such methods require preconditioners that preserve
		  certain sub-blocks from the original systems but allow
		  considerable flexibility for the remaining blocks. We
		  construct a number of families of implicit factorizations
		  that are capable of reproducing the required sub-blocks and
		  (some) of the remainder. These generalize known implicit
		  factorizations for the unregularized case. Improved
		  eigenvalue clustering is possible if additionally some of
		  the noncrucial blocks are reproduced. Numerical experiments
		  confirm that these implicit-factorization preconditioners
		  can be very effective in practice. },
  author	= {H. S. Dollar and N. Gould and W. Schilders and A. J.
		  Wathen},
  doi		= {10.1137/05063427X},
  journal	= siammath,
  keywords	= {regularized saddle-point systems; implicit-factorization
		  preconditioners},
  number	= 1,
  pages		= {170-189},
  publisher	= {SIAM},
  title		= {Implicit-Factorization Preconditioning and Iterative
		  Solvers for Regularized Saddle-Point Systems},
  url		= {http://link.aip.org/link/?SML/28/170/1},
  volume	= 28,
  year		= 2006
}

###Article{	  dollgoulschiwath:2006,
  abstract	= {We consider conjugate-gradient like methods for solving
		  block symmetric indefinite linear systems that arise from
		  saddle-point problems or, in particular, regularizations
		  thereof. Such methods require preconditioners that preserve
		  certain sub-blocks from the original systems but allow
		  considerable flexibility for the remaining blocks. We
		  construct a number of families of implicit factorizations
		  that are capable of reproducing the required sub-blocks and
		  (some) of the remainder. These generalize known implicit
		  factorizations for the unregularized case. Improved
		  eigenvalue clustering is possible if additionally some of
		  the noncrucial blocks are reproduced. Numerical experiments
		  confirm that these implicit-factorization preconditioners
		  can be very effective in practice. },
  author	= {H. S. Dollar and N. Gould and W. Schilders and A. J.
		  Wathen},
  doi		= {10.1137/05063427X},
  journal	= siammath,
  keywords	= {regularized saddle-point systems; implicit-factorization
		  preconditioners},
  number	= 1,
  pages		= {170-189},
  publisher	= {SIAM},
  title		= {Implicit-Factorization Preconditioning and Iterative
		  Solvers for Regularized Saddle-Point Systems},
  url		= {http://link.aip.org/link/?SML/28/170/1},
  volume	= 28,
  year		= 2006
}

@TechReport{	  don06a,
  author	= "Donoho, D. L.",
  title		= "For Most Large Underdetermined Systems of Linear Equations
		  the Minimal l1-norm Near-Solution Approximates the Sparsest
		  near-Solution",
  institution	= "Department of Statistics, Stanford University, Stanford",
  year		= 2006
}

###TechReport{	  don06a,
  author	= "Donoho, D. L.",
  title		= "For Most Large Underdetermined Systems of Linear Equations
		  the Minimal l1-norm Near-Solution Approximates the Sparsest
		  near-Solution",
  institution	= "Department of Statistics, Stanford University, Stanford",
  year		= 2006
}

###TechReport{	  don06a,
  author	= "Donoho, D. L.",
  title		= "For Most Large Underdetermined Systems of Linear Equations
		  the Minimal l1-norm Near-Solution Approximates the Sparsest
		  near-Solution",
  institution	= "Department of Statistics, Stanford University, Stanford",
  year		= 2006
}

@Article{	  don06b,
  author	= "Donoho, D. L.",
  title		= "For Most Large Underdetermined Systems of Linear
		  Equations, the Minimal l1-norm Solution is also the
		  Sparsest Solution",
  journal	= "Communications on Pure and Applied Mathematics",
  year		= 2006,
  volume	= 59,
  page		= {797--929}
}

###Article{	  don06b,
  author	= "Donoho, D. L.",
  title		= "For Most Large Underdetermined Systems of Linear
		  Equations, the Minimal l1-norm Solution is also the
		  Sparsest Solution",
  journal	= "Communications on Pure and Applied Mathematics",
  year		= 2006,
  volume	= 59,
  page		= {797--929}
}

###Article{	  don06b,
  author	= "Donoho, D. L.",
  title		= "For Most Large Underdetermined Systems of Linear
		  Equations, the Minimal l1-norm Solution is also the
		  Sparsest Solution",
  journal	= "Communications on Pure and Applied Mathematics",
  year		= 2006,
  volume	= 59,
  page		= {797--929}
}

@Article{	  don06c,
  author	= "Donoho, D. L., Elad, M., and Temlyakov, V. N.",
  title		= "Stable recovery of sparse overcomplete representations in
		  the presence of noise",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2006,
  volume	= 52,
  page		= {6--18}
}

###Article{	  don06c,
  author	= "Donoho, D. L., Elad, M., and Temlyakov, V. N.",
  title		= "Stable recovery of sparse overcomplete representations in
		  the presence of noise",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2006,
  volume	= 52,
  page		= {6--18}
}

###Article{	  don06c,
  author	= "Donoho, D. L., Elad, M., and Temlyakov, V. N.",
  title		= "Stable recovery of sparse overcomplete representations in
		  the presence of noise",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2006,
  volume	= 52,
  page		= {6--18}
}

@Article{	  dono:2006b,
  author	= {D. L. Donoho},
  journal	= ieeetransinfo,
  month		= {April},
  number	= 4,
  pages		= {1289--1306},
  title		= {Compressed sensing},
  volume	= 52,
  year		= 2006
}

###Article{	  dono:2006b,
  author	= {D. L. Donoho},
  journal	= ieeetransinfo,
  month		= {April},
  number	= 4,
  pages		= {1289--1306},
  title		= {Compressed sensing},
  volume	= 52,
  year		= 2006
}

###Article{	  dono:2006b,
  author	= {D. L. Donoho},
  journal	= ieeetransinfo,
  month		= {April},
  number	= 4,
  pages		= {1289--1306},
  title		= {Compressed sensing},
  volume	= 52,
  year		= 2006
}

@Article{	  donoho1994,
  author	= "Donoho, D. L. and Johnstone, I. M.",
  title		= "Ideal spatial adaptation by wavelet shrinkage",
  journal	= "Biometrika",
  year		= 1994,
  volume	= 81,
  page		= {425--455}
}

###Article{	  donoho1994,
  author	= "Donoho, D. L. and Johnstone, I. M.",
  title		= "Ideal spatial adaptation by wavelet shrinkage",
  journal	= "Biometrika",
  year		= 1994,
  volume	= 81,
  page		= {425--455}
}

###Article{	  donoho1994,
  author	= "Donoho, D. L. and Johnstone, I. M.",
  title		= "Ideal spatial adaptation by wavelet shrinkage",
  journal	= "Biometrika",
  year		= 1994,
  volume	= 81,
  page		= {425--455}
}

@Article{	  donoho2001,
  author	= "Donoho. D. L. and Huo, X.",
  title		= "Uncertainty principles and ideal atomic decomposition",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2001,
  volume	= 47,
  page		= {2845--2862}
}

###Article{	  donoho2001,
  author	= "Donoho. D. L. and Huo, X.",
  title		= "Uncertainty principles and ideal atomic decomposition",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2001,
  volume	= 47,
  page		= {2845--2862}
}

###Article{	  donoho2001,
  author	= "Donoho. D. L. and Huo, X.",
  title		= "Uncertainty principles and ideal atomic decomposition",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2001,
  volume	= 47,
  page		= {2845--2862}
}

@Proceedings{	  donoho2003,
  author	= "Donoho, D. L. and Elad, M.",
  title		= "Optimally sparse representation in general (nonorthogonal)
		  dictionaries via $\ell-1$ minimization",
  year		= 2003,
  organization	= "Nat. Acad. Sci.",
  volume	= 100,
  page		= {2197--2202}
}

###Proceedings{	  donoho2003,
  author	= "Donoho, D. L. and Elad, M.",
  title		= "Optimally sparse representation in general (nonorthogonal)
		  dictionaries via $\ell-1$ minimization",
  year		= 2003,
  organization	= "Nat. Acad. Sci.",
  volume	= 100,
  page		= {2197--2202}
}

###Proceedings{	  donoho2003,
  author	= "Donoho, D. L. and Elad, M.",
  title		= "Optimally sparse representation in general (nonorthogonal)
		  dictionaries via $\ell-1$ minimization",
  year		= 2003,
  organization	= "Nat. Acad. Sci.",
  volume	= 100,
  page		= {2197--2202}
}

@TechReport{	  donoho2006d,
  author	= "Donoho, D. L., Tsaig, Y., Drori, I., and Starek, J.-L.",
  title		= "Sparse solution of underdetermined linear equations by
		  stagewise orthogonal matching pursuit",
  institution	= "Department of Statistics, Stanford University, Stanford",
  year		= 2006
}

###TechReport{	  donoho2006d,
  author	= "Donoho, D. L., Tsaig, Y., Drori, I., and Starek, J.-L.",
  title		= "Sparse solution of underdetermined linear equations by
		  stagewise orthogonal matching pursuit",
  institution	= "Department of Statistics, Stanford University, Stanford",
  year		= 2006
}

###TechReport{	  donoho2006d,
  author	= "Donoho, D. L., Tsaig, Y., Drori, I., and Starek, J.-L.",
  title		= "Sparse solution of underdetermined linear equations by
		  stagewise orthogonal matching pursuit",
  institution	= "Department of Statistics, Stanford University, Stanford",
  year		= 2006
}

@Article{	  donotann:2005b,
  author	= {David L. Dohono and Jared Tanner},
  title		= {Neighborliness of randomly-projected simplices in high
		  dimensions},
  journal	= procnas,
  volume	= 102,
  number	= 27,
  year		= 2005,
  pages		= {9452--9457},
  url		= {http://www.pnas.org/cgi/content/abstract/102/27/9452},
  eprint	= {http://www.pnas.org/cgi/reprint/102/27/9452},
  keywords	= {Neighborly polytopes, convex hull of Gaussian sample,
		  underdetermined systems of linear equations, uniformly
		  distributed random projections, phase transitions},
  abstract	= {Let $A$ be a $d \times n$ matrix and $T = T^{n-1}$ be the
		  standard simplex in $R^n$. Suppose that $d$ and $n$ are
		  both large and comparable: $d \approx \delta n$, $\delta
		  \in (0,1)$. We count the faces of the projected simplex
		  $AT$ when the projector $A$ is chosen uniformly at random
		  from the Grassmann manifold of $d$-dimensional
		  orthoprojectors of $R^n$. We derive $\rho_N(\delta)> 0$
		  with the property that, for any $\rho < \rho_N(\delta)$,
		  with overwhelming probability for large $d$, the number of
		  $k$-dimensional faces of $P = AT$ is exactly the same as
		  for $T$, for $0 \leq k \leq \rho d$. This implies that $P$
		  is $\lfloor\rho d\rfloor$-neighborly, and its skeleton
		  $\mathrm{Skel}_{\lfloor\rho d\rfloor}(P)$ is
		  combinatorially equivalent to $\mathrm{Skel}_{\lfloor\rho
		  d\rfloor}(T)$. We also study a weaker notion of
		  neighborliness where the numbers of $k$-dimensional faces
		  $f_k(P) \geq f_k(T)(1-\varepsilon)$. Vershik and Sporyshev
		  previously showed existence of a threshold
		  $\rho_{VS}(\delta) > 0$ at which phase transition occurs in
		  $k/d$. We compute and display $\rho_{VS}$ and compare with
		  $\rho_N$. Corollaries are as follows. (1) The convex hull
		  of $n$ Gaussian samples in $R^d$, with $n$ large and
		  proportional to $d$, has the same $k$-skeleton as the $(n -
		  1)$ simplex, for $k < \rho_N (d/n)d(1 + o_P(1))$. (2) There
		  is a ``phase transition'' in the ability of linear
		  programming to find the sparsest nonnegative solution to
		  systems of underdetermined linear equations. For most
		  systems having a solution with fewer than
		  $\rho_{VS}(d/n)d(1+o(1))$ nonzeros, linear programming will find that solution.}
}

###Article{	  donotann:2005b,
  author	= {David L. Dohono and Jared Tanner},
  title		= {Neighborliness of randomly-projected simplices in high
		  dimensions},
  journal	= procnas,
  volume	= 102,
  number	= 27,
  year		= 2005,
  pages		= {9452--9457},
  url		= {http://www.pnas.org/cgi/content/abstract/102/27/9452},
  eprint	= {http://www.pnas.org/cgi/reprint/102/27/9452},
  keywords	= {Neighborly polytopes, convex hull of Gaussian sample,
		  underdetermined systems of linear equations, uniformly
		  distributed random projections, phase transitions},
  abstract	= {Let $A$ be a $d \times n$ matrix and $T = T^{n-1}$ be the
		  standard simplex in $R^n$. Suppose that $d$ and $n$ are
		  both large and comparable: $d \approx \delta n$, $\delta
		  \in (0,1)$. We count the faces of the projected simplex
		  $AT$ when the projector $A$ is chosen uniformly at random
		  from the Grassmann manifold of $d$-dimensional
		  orthoprojectors of $R^n$. We derive $\rho_N(\delta)> 0$
		  with the property that, for any $\rho < \rho_N(\delta)$,
		  with overwhelming probability for large $d$, the number of
		  $k$-dimensional faces of $P = AT$ is exactly the same as
		  for $T$, for $0 \leq k \leq \rho d$. This implies that $P$
		  is $\lfloor\rho d\rfloor$-neighborly, and its skeleton
		  $\mathrm{Skel}_{\lfloor\rho d\rfloor}(P)$ is
		  combinatorially equivalent to $\mathrm{Skel}_{\lfloor\rho
		  d\rfloor}(T)$. We also study a weaker notion of
		  neighborliness where the numbers of $k$-dimensional faces
		  $f_k(P) \geq f_k(T)(1-\varepsilon)$. Vershik and Sporyshev
		  previously showed existence of a threshold
		  $\rho_{VS}(\delta) > 0$ at which phase transition occurs in
		  $k/d$. We compute and display $\rho_{VS}$ and compare with
		  $\rho_N$. Corollaries are as follows. (1) The convex hull
		  of $n$ Gaussian samples in $R^d$, with $n$ large and
		  proportional to $d$, has the same $k$-skeleton as the $(n -
		  1)$ simplex, for $k < \rho_N (d/n)d(1 + o_P(1))$. (2) There
		  is a ``phase transition'' in the ability of linear
		  programming to find the sparsest nonnegative solution to
		  systems of underdetermined linear equations. For most
		  systems having a solution with fewer than
		  $\rho_{VS}(d/n)d(1+o(1))$ nonzeros, linear programming will find that solution.}
}

###Article{	  donotann:2005b,
  author	= {David L. Dohono and Jared Tanner},
  title		= {Neighborliness of randomly-projected simplices in high
		  dimensions},
  journal	= procnas,
  volume	= 102,
  number	= 27,
  year		= 2005,
  pages		= {9452--9457},
  url		= {http://www.pnas.org/cgi/content/abstract/102/27/9452},
  eprint	= {http://www.pnas.org/cgi/reprint/102/27/9452},
  keywords	= {Neighborly polytopes, convex hull of Gaussian sample,
		  underdetermined systems of linear equations, uniformly
		  distributed random projections, phase transitions},
  abstract	= {Let $A$ be a $d \times n$ matrix and $T = T^{n-1}$ be the
		  standard simplex in $R^n$. Suppose that $d$ and $n$ are
		  both large and comparable: $d \approx \delta n$, $\delta
		  \in (0,1)$. We count the faces of the projected simplex
		  $AT$ when the projector $A$ is chosen uniformly at random
		  from the Grassmann manifold of $d$-dimensional
		  orthoprojectors of $R^n$. We derive $\rho_N(\delta)> 0$
		  with the property that, for any $\rho < \rho_N(\delta)$,
		  with overwhelming probability for large $d$, the number of
		  $k$-dimensional faces of $P = AT$ is exactly the same as
		  for $T$, for $0 \leq k \leq \rho d$. This implies that $P$
		  is $\lfloor\rho d\rfloor$-neighborly, and its skeleton
		  $\mathrm{Skel}_{\lfloor\rho d\rfloor}(P)$ is
		  combinatorially equivalent to $\mathrm{Skel}_{\lfloor\rho
		  d\rfloor}(T)$. We also study a weaker notion of
		  neighborliness where the numbers of $k$-dimensional faces
		  $f_k(P) \geq f_k(T)(1-\varepsilon)$. Vershik and Sporyshev
		  previously showed existence of a threshold
		  $\rho_{VS}(\delta) > 0$ at which phase transition occurs in
		  $k/d$. We compute and display $\rho_{VS}$ and compare with
		  $\rho_N$. Corollaries are as follows. (1) The convex hull
		  of $n$ Gaussian samples in $R^d$, with $n$ large and
		  proportional to $d$, has the same $k$-skeleton as the $(n -
		  1)$ simplex, for $k < \rho_N (d/n)d(1 + o_P(1))$. (2) There
		  is a ``phase transition'' in the ability of linear
		  programming to find the sparsest nonnegative solution to
		  systems of underdetermined linear equations. For most
		  systems having a solution with fewer than
		  $\rho_{VS}(d/n)d(1+o(1))$ nonzeros, linear programming will find that solution.}
}

@Unpublished{	  donotsai:2006,
  author	= {David L. Donoho and Yaakov Tsaig},
  month		= {October},
  note		= {\url{http://www.stanford.edu/~tsaig/research.html}},
  title		= {Fast solution of {L1}-norm minimization problems when the
		  solution may be sparse},
  year		= 2006
}

###Unpublished{	  donotsai:2006,
  author	= {David L. Donoho and Yaakov Tsaig},
  month		= {October},
  note		= {\url{http://www.stanford.edu/~tsaig/research.html}},
  title		= {Fast solution of {L1}-norm minimization problems when the
		  solution may be sparse},
  year		= 2006
}

###Unpublished{	  donotsai:2006,
  author	= {David L. Donoho and Yaakov Tsaig},
  month		= {October},
  note		= {\url{http://www.stanford.edu/~tsaig/research.html}},
  title		= {Fast solution of {L1}-norm minimization problems when the
		  solution may be sparse},
  year		= 2006
}

@Article{	  dost:1991,
  abstract	= {An upper bound for the difference of the exact solution of
		  the problem of minimization of a quadratic functional on
		  the subspace and its penalty approximation is given. The
		  paper supplies a numerical example.},
  author	= {Z. Dost\'{a}l},
  journal	= {Kybernetika},
  number	= 2,
  pages		= {151--154},
  title		= {On the penalty approximation of quadratic programming
		  problem},
  volume	= 27,
  year		= 1991
}

###Article{	  dost:1991,
  abstract	= {An upper bound for the difference of the exact solution of
		  the problem of minimization of a quadratic functional on
		  the subspace and its penalty approximation is given. The
		  paper supplies a numerical example.},
  author	= {Z. Dost\'{a}l},
  journal	= {Kybernetika},
  number	= 2,
  pages		= {151--154},
  title		= {On the penalty approximation of quadratic programming
		  problem},
  volume	= 27,
  year		= 1991
}

###Article{	  dost:1991,
  abstract	= {An upper bound for the difference of the exact solution of
		  the problem of minimization of a quadratic functional on
		  the subspace and its penalty approximation is given. The
		  paper supplies a numerical example.},
  author	= {Z. Dost\'{a}l},
  journal	= {Kybernetika},
  number	= 2,
  pages		= {151--154},
  title		= {On the penalty approximation of quadratic programming
		  problem},
  volume	= 27,
  year		= 1991
}

@Article{	  dost:1996,
  abstract	= {We review our recent results on the solution of quadratic
		  programming problems with simple bounds by means of the
		  conjugate gradient method with inexact solution of
		  auxiliary subproblems and projections. Precision of the
		  solution of auxiliary problems is controlled by the product
		  of a positive constant Gamma with the norm of violation of
		  the Kuhn-Tucker contact conditions. The resulting algorithm
		  converges for any positive Gamma and reaches the solution
		  in a finite number of steps provided the problem is
		  nondegenerate. A lower bound on Gamma is given so that the
		  finite termination property is preserved even for
		  degenerate problems. The algorithm may be implemented with
		  projections so that it can drop and add many constraints
		  whenever the active set is changed. Applications to the
		  solution of inner obstacle problems and contact problems of
		  elasticity are reported.},
  author	= {Z. Dost\'{a}l},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Zeitschrift f\"{u}r Angewandte Mathematik und Mechanik},
  number	= {S3},
  pages		= {413--414},
  title		= {Box constrained quadratic programming with controlled
		  precision of auxiliary problems and applications},
  volume	= 76,
  year		= 1996
}

###Article{	  dost:1996,
  abstract	= {We review our recent results on the solution of quadratic
		  programming problems with simple bounds by means of the
		  conjugate gradient method with inexact solution of
		  auxiliary subproblems and projections. Precision of the
		  solution of auxiliary problems is controlled by the product
		  of a positive constant Gamma with the norm of violation of
		  the Kuhn-Tucker contact conditions. The resulting algorithm
		  converges for any positive Gamma and reaches the solution
		  in a finite number of steps provided the problem is
		  nondegenerate. A lower bound on Gamma is given so that the
		  finite termination property is preserved even for
		  degenerate problems. The algorithm may be implemented with
		  projections so that it can drop and add many constraints
		  whenever the active set is changed. Applications to the
		  solution of inner obstacle problems and contact problems of
		  elasticity are reported.},
  author	= {Z. Dost\'{a}l},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Zeitschrift f\"{u}r Angewandte Mathematik und Mechanik},
  number	= {S3},
  pages		= {413--414},
  title		= {Box constrained quadratic programming with controlled
		  precision of auxiliary problems and applications},
  volume	= 76,
  year		= 1996
}

###Article{	  dost:1996,
  abstract	= {We review our recent results on the solution of quadratic
		  programming problems with simple bounds by means of the
		  conjugate gradient method with inexact solution of
		  auxiliary subproblems and projections. Precision of the
		  solution of auxiliary problems is controlled by the product
		  of a positive constant Gamma with the norm of violation of
		  the Kuhn-Tucker contact conditions. The resulting algorithm
		  converges for any positive Gamma and reaches the solution
		  in a finite number of steps provided the problem is
		  nondegenerate. A lower bound on Gamma is given so that the
		  finite termination property is preserved even for
		  degenerate problems. The algorithm may be implemented with
		  projections so that it can drop and add many constraints
		  whenever the active set is changed. Applications to the
		  solution of inner obstacle problems and contact problems of
		  elasticity are reported.},
  author	= {Z. Dost\'{a}l},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Zeitschrift f\"{u}r Angewandte Mathematik und Mechanik},
  number	= {S3},
  pages		= {413--414},
  title		= {Box constrained quadratic programming with controlled
		  precision of auxiliary problems and applications},
  volume	= 76,
  year		= 1996
}

@Article{	  dost:1997,
  abstract	= {Two new closely related concepts are introduced that
		  depend on a positive constant Gamma . An iteration is
		  proportional if the norm of violation of the Kuhn-Tucker
		  conditions at active variables does not excessively exceed
		  the norm of the part of the gradient that corresponds to
		  free variables, while a progressive direction determines a
		  descent direction that enables the released variables to
		  move far enough from the boundary in a step called
		  proportioning. An algorithm that uses the conjugate
		  gradient method to explore the face of the region defined
		  by the current iterate until a disproportional iteration is
		  generated is proposed. It then changes the face by means of
		  the progressive direction. It is proved that for strictly
		  convex problems, the proportioning is a spacer iteration so
		  that the algorithm converges to the solution. If the
		  solution is nondegenerate then the algorithm finds the
		  solution in a finite number of steps. Moreover, a simple
		  lower bound on Gamma is given to ensure finite termination
		  even for problems with degenerate solutions. The theory
		  covers a class of algorithms, allowing many constraints to
		  be added or dropped at a time and accepting approximate
		  solutions of auxiliary problems. Preliminary numerical
		  results are promising.},
  author	= {Z. Dost\'{a}l},
  journal	= siamopt,
  number	= 3,
  pages		= {871--887},
  title		= {Box constrained quadratic programming with proportioning
		  and projections},
  volume	= 7,
  year		= 1997
}

###Article{	  dost:1997,
  abstract	= {Two new closely related concepts are introduced that
		  depend on a positive constant Gamma . An iteration is
		  proportional if the norm of violation of the Kuhn-Tucker
		  conditions at active variables does not excessively exceed
		  the norm of the part of the gradient that corresponds to
		  free variables, while a progressive direction determines a
		  descent direction that enables the released variables to
		  move far enough from the boundary in a step called
		  proportioning. An algorithm that uses the conjugate
		  gradient method to explore the face of the region defined
		  by the current iterate until a disproportional iteration is
		  generated is proposed. It then changes the face by means of
		  the progressive direction. It is proved that for strictly
		  convex problems, the proportioning is a spacer iteration so
		  that the algorithm converges to the solution. If the
		  solution is nondegenerate then the algorithm finds the
		  solution in a finite number of steps. Moreover, a simple
		  lower bound on Gamma is given to ensure finite termination
		  even for problems with degenerate solutions. The theory
		  covers a class of algorithms, allowing many constraints to
		  be added or dropped at a time and accepting approximate
		  solutions of auxiliary problems. Preliminary numerical
		  results are promising.},
  author	= {Z. Dost\'{a}l},
  journal	= siamopt,
  number	= 3,
  pages		= {871--887},
  title		= {Box constrained quadratic programming with proportioning
		  and projections},
  volume	= 7,
  year		= 1997
}

###Article{	  dost:1997,
  abstract	= {Two new closely related concepts are introduced that
		  depend on a positive constant Gamma . An iteration is
		  proportional if the norm of violation of the Kuhn-Tucker
		  conditions at active variables does not excessively exceed
		  the norm of the part of the gradient that corresponds to
		  free variables, while a progressive direction determines a
		  descent direction that enables the released variables to
		  move far enough from the boundary in a step called
		  proportioning. An algorithm that uses the conjugate
		  gradient method to explore the face of the region defined
		  by the current iterate until a disproportional iteration is
		  generated is proposed. It then changes the face by means of
		  the progressive direction. It is proved that for strictly
		  convex problems, the proportioning is a spacer iteration so
		  that the algorithm converges to the solution. If the
		  solution is nondegenerate then the algorithm finds the
		  solution in a finite number of steps. Moreover, a simple
		  lower bound on Gamma is given to ensure finite termination
		  even for problems with degenerate solutions. The theory
		  covers a class of algorithms, allowing many constraints to
		  be added or dropped at a time and accepting approximate
		  solutions of auxiliary problems. Preliminary numerical
		  results are promising.},
  author	= {Z. Dost\'{a}l},
  journal	= siamopt,
  number	= 3,
  pages		= {871--887},
  title		= {Box constrained quadratic programming with proportioning
		  and projections},
  volume	= 7,
  year		= 1997
}

@InProceedings{	  dostfriesant:1998,
  address	= {Dordrecht, The Netherlands},
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  booktitle	= {High Performance Algorithms and Software in Nonlinear
		  Optimization},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {R. De Leone and A. Murli and P. M. Pardalos and G.
		  Toraldo},
  pages		= {161--173},
  publisher	= {Kluwer Academic},
  title		= {Adaptive precision control in quadratic programming with
		  simple bounds and/or equality constraints},
  year		= 1998
}

###InProceedings{ dostfriesant:1998,
  address	= {Dordrecht, The Netherlands},
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  booktitle	= {High Performance Algorithms and Software in Nonlinear
		  Optimization},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {R. De Leone and A. Murli and P. M. Pardalos and G.
		  Toraldo},
  pages		= {161--173},
  publisher	= {Kluwer Academic},
  title		= {Adaptive precision control in quadratic programming with
		  simple bounds and/or equality constraints},
  year		= 1998
}

###InProceedings{ dostfriesant:1998,
  address	= {Dordrecht, The Netherlands},
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  booktitle	= {High Performance Algorithms and Software in Nonlinear
		  Optimization},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {R. De Leone and A. Murli and P. M. Pardalos and G.
		  Toraldo},
  pages		= {161--173},
  publisher	= {Kluwer Academic},
  title		= {Adaptive precision control in quadratic programming with
		  simple bounds and/or equality constraints},
  year		= 1998
}

@Article{	  dostfriesant:1999,
  abstract	= {In this paper we introduce an augmented Lagrangian type
		  algorithm for strictly convex quadratic programming
		  problems with equality constraints. The new feature of the
		  proposed algorithm is the adaptive precision control of the
		  solution of auxiliary problems in the inner loop of the
		  basic algorithm. Global convergence and boundedness of the
		  penalty parameter are proved and an error estimate is given
		  that does not have any term that accounts for the inexact
		  solution of the auxiliary problems. Numerical experiments
		  illustrate efficiency of the algorithm presented.},
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= compapplopt,
  local-url	= {file://localhost/Users/mpf/papers/DostFrieSant99.pdf},
  number	= 1,
  pages		= {37--53},
  title		= {Augmented {L}agrangians with adaptive precision control
		  for quadratic programming with equality constraints},
  volume	= 14,
  year		= 1999
}

###Article{	  dostfriesant:1999,
  abstract	= {In this paper we introduce an augmented Lagrangian type
		  algorithm for strictly convex quadratic programming
		  problems with equality constraints. The new feature of the
		  proposed algorithm is the adaptive precision control of the
		  solution of auxiliary problems in the inner loop of the
		  basic algorithm. Global convergence and boundedness of the
		  penalty parameter are proved and an error estimate is given
		  that does not have any term that accounts for the inexact
		  solution of the auxiliary problems. Numerical experiments
		  illustrate efficiency of the algorithm presented.},
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= compapplopt,
  local-url	= {file://localhost/Users/mpf/papers/DostFrieSant99.pdf},
  number	= 1,
  pages		= {37--53},
  title		= {Augmented {L}agrangians with adaptive precision control
		  for quadratic programming with equality constraints},
  volume	= 14,
  year		= 1999
}

###Article{	  dostfriesant:1999,
  abstract	= {In this paper we introduce an augmented Lagrangian type
		  algorithm for strictly convex quadratic programming
		  problems with equality constraints. The new feature of the
		  proposed algorithm is the adaptive precision control of the
		  solution of auxiliary problems in the inner loop of the
		  basic algorithm. Global convergence and boundedness of the
		  penalty parameter are proved and an error estimate is given
		  that does not have any term that accounts for the inexact
		  solution of the auxiliary problems. Numerical experiments
		  illustrate efficiency of the algorithm presented.},
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= compapplopt,
  local-url	= {file://localhost/Users/mpf/papers/DostFrieSant99.pdf},
  number	= 1,
  pages		= {37--53},
  title		= {Augmented {L}agrangians with adaptive precision control
		  for quadratic programming with equality constraints},
  volume	= 14,
  year		= 1999
}

@Article{	  dostfriesant:2003,
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  journal	= siamopt,
  local-url	= {file://localhost/Users/mpf/papers/DostFrieSant03.pdf},
  number	= 4,
  pages		= {1120--1140},
  title		= {Augmented {L}agrangians with adaptive precision control
		  for quadratic programming with simple bounds and equality
		  constraints},
  volume	= 13,
  year		= 2003
}

###Article{	  dostfriesant:2003,
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  journal	= siamopt,
  local-url	= {file://localhost/Users/mpf/papers/DostFrieSant03.pdf},
  number	= 4,
  pages		= {1120--1140},
  title		= {Augmented {L}agrangians with adaptive precision control
		  for quadratic programming with simple bounds and equality
		  constraints},
  volume	= 13,
  year		= 2003
}

###Article{	  dostfriesant:2003,
  author	= {Z. Dost\'{a}l and A. Friedlander and S. A. Santos},
  journal	= siamopt,
  local-url	= {file://localhost/Users/mpf/papers/DostFrieSant03.pdf},
  number	= 4,
  pages		= {1120--1140},
  title		= {Augmented {L}agrangians with adaptive precision control
		  for quadratic programming with simple bounds and equality
		  constraints},
  volume	= 13,
  year		= 2003
}

@Article{	  dot05,
  abstract	= {Consider an underdetermined system of linear equations y =
		  Ax with known y and d x n matrix A. We seek the nonnegative
		  x with the fewest nonzeros satisfying y = Ax. In general,
		  this problem is NP-hard. However, for many matrices A there
		  is a threshold phenomenon: if the sparsest solution is
		  sufficiently sparse, it can be found by linear programming.
		  We explain this by the theory of convex polytopes. Let aj
		  denote the jth column of A, 1 [&le;] j [&le;] n, let a0 = 0
		  and P denote the convex hull of the aj. We say the polytope
		  P is outwardly k-neighborly if every subset of k vertices
		  not including 0 spans a face of P. We show that outward
		  k-neighborliness is equivalent to the statement that,
		  whenever y = Ax has a nonnegative solution with at most k
		  nonzeros, it is the nonnegative solution to y = Ax having
		  minimal sum. We also consider weak neighborliness, where
		  the overwhelming majority of k-sets of ajs not containing 0
		  span a face of P. This implies that most nonnegative
		  vectors x with k nonzeros are uniquely recoverable from y =
		  Ax by linear programming. Numerous corollaries follow by
		  invoking neighborliness results. For example, for most
		  large n by 2n underdetermined systems having a solution
		  with fewer nonzeros than roughly half the number of
		  equations, the sparsest solution can be found by linear
		  programming. },
  author	= {D. L. Donoho and J. Tanner},
  date-modified	= {2007-07-18 14:59:34 -0700},
  eprint	= {http://www.pnas.org/cgi/reprint/102/27/9446.pdf},
  journal	= procnas,
  number	= 27,
  pages		= {9446-9451},
  title		= {Sparse nonnegative solution of underdetermined linear
		  equations by linear programming},
  url		= {http://www.pnas.org/cgi/content/abstract/102/27/9446},
  volume	= 102,
  year		= 2005
}

###Article{	  dot05,
  abstract	= {Consider an underdetermined system of linear equations y =
		  Ax with known y and d x n matrix A. We seek the nonnegative
		  x with the fewest nonzeros satisfying y = Ax. In general,
		  this problem is NP-hard. However, for many matrices A there
		  is a threshold phenomenon: if the sparsest solution is
		  sufficiently sparse, it can be found by linear programming.
		  We explain this by the theory of convex polytopes. Let aj
		  denote the jth column of A, 1 [&le;] j [&le;] n, let a0 = 0
		  and P denote the convex hull of the aj. We say the polytope
		  P is outwardly k-neighborly if every subset of k vertices
		  not including 0 spans a face of P. We show that outward
		  k-neighborliness is equivalent to the statement that,
		  whenever y = Ax has a nonnegative solution with at most k
		  nonzeros, it is the nonnegative solution to y = Ax having
		  minimal sum. We also consider weak neighborliness, where
		  the overwhelming majority of k-sets of ajs not containing 0
		  span a face of P. This implies that most nonnegative
		  vectors x with k nonzeros are uniquely recoverable from y =
		  Ax by linear programming. Numerous corollaries follow by
		  invoking neighborliness results. For example, for most
		  large n by 2n underdetermined systems having a solution
		  with fewer nonzeros than roughly half the number of
		  equations, the sparsest solution can be found by linear
		  programming. },
  author	= {D. L. Donoho and J. Tanner},
  date-modified	= {2007-07-18 14:59:34 -0700},
  eprint	= {http://www.pnas.org/cgi/reprint/102/27/9446.pdf},
  journal	= procnas,
  number	= 27,
  pages		= {9446-9451},
  title		= {Sparse nonnegative solution of underdetermined linear
		  equations by linear programming},
  url		= {http://www.pnas.org/cgi/content/abstract/102/27/9446},
  volume	= 102,
  year		= 2005
}

###Article{	  dot05,
  abstract	= {Consider an underdetermined system of linear equations y =
		  Ax with known y and d x n matrix A. We seek the nonnegative
		  x with the fewest nonzeros satisfying y = Ax. In general,
		  this problem is NP-hard. However, for many matrices A there
		  is a threshold phenomenon: if the sparsest solution is
		  sufficiently sparse, it can be found by linear programming.
		  We explain this by the theory of convex polytopes. Let aj
		  denote the jth column of A, 1 [&le;] j [&le;] n, let a0 = 0
		  and P denote the convex hull of the aj. We say the polytope
		  P is outwardly k-neighborly if every subset of k vertices
		  not including 0 spans a face of P. We show that outward
		  k-neighborliness is equivalent to the statement that,
		  whenever y = Ax has a nonnegative solution with at most k
		  nonzeros, it is the nonnegative solution to y = Ax having
		  minimal sum. We also consider weak neighborliness, where
		  the overwhelming majority of k-sets of ajs not containing 0
		  span a face of P. This implies that most nonnegative
		  vectors x with k nonzeros are uniquely recoverable from y =
		  Ax by linear programming. Numerous corollaries follow by
		  invoking neighborliness results. For example, for most
		  large n by 2n underdetermined systems having a solution
		  with fewer nonzeros than roughly half the number of
		  equations, the sparsest solution can be found by linear
		  programming. },
  author	= {D. L. Donoho and J. Tanner},
  date-modified	= {2007-07-18 14:59:34 -0700},
  eprint	= {http://www.pnas.org/cgi/reprint/102/27/9446.pdf},
  journal	= procnas,
  number	= 27,
  pages		= {9446-9451},
  title		= {Sparse nonnegative solution of underdetermined linear
		  equations by linear programming},
  url		= {http://www.pnas.org/cgi/content/abstract/102/27/9446},
  volume	= 102,
  year		= 2005
}

@InProceedings{	  dpe01,
  author	= "Doherty, L. and Pister, K. S. J. and El Ghaoui, L.",
  title		= "Convex position estimation in wireless sensor networks",
  year		= 2001,
  booktitle	= "20th INFOCOM",
  volume	= 3,
  page		= {1655--1663}
}

###InProceedings{ dpe01,
  author	= "Doherty, L. and Pister, K. S. J. and El Ghaoui, L.",
  title		= "Convex position estimation in wireless sensor networks",
  year		= 2001,
  booktitle	= "20th INFOCOM",
  volume	= 3,
  page		= {1655--1663}
}

###InProceedings{ dpe01,
  author	= "Doherty, L. and Pister, K. S. J. and El Ghaoui, L.",
  title		= "Convex position estimation in wireless sensor networks",
  year		= 2001,
  booktitle	= "20th INFOCOM",
  volume	= 3,
  page		= {1655--1663}
}

@Article{	  drud:1994,
  author	= {A. S. Drud},
  journal	= {{ORSA} J. Computing},
  pages		= {207--216},
  title		= {A large scale {GRG} code},
  volume	= 6,
  year		= 1994
}

###Article{	  drud:1994,
  author	= {A. S. Drud},
  journal	= {{ORSA} J. Computing},
  pages		= {207--216},
  title		= {A large scale {GRG} code},
  volume	= 6,
  year		= 1994
}

###Article{	  drud:1994,
  author	= {A. S. Drud},
  journal	= {{ORSA} J. Computing},
  pages		= {207--216},
  title		= {A large scale {GRG} code},
  volume	= 6,
  year		= 1994
}

@Article{	  drusvyatskiy2016optimal,
  title		= {An optimal first order method based on optimal quadratic
		  averaging},
  author	= {Drusvyatskiy, Dmitriy and Fazel, Maryam and Roy, Scott},
  journal	= {arXiv preprint arXiv:1604.06543},
  year		= {2016}
}

@Misc{		  dst07,
  author	= {David L. Donoho and V. C. Stodden and Yaakov Tsaig},
  howpublished	= {\url{http://sparselab.stanford.edu/}},
  title		= {Sparselab},
  year		= 2007
}

###Misc{	  dst07,
  author	= {David L. Donoho and V. C. Stodden and Yaakov Tsaig},
  howpublished	= {\url{http://sparselab.stanford.edu/}},
  title		= {Sparselab},
  year		= 2007
}

###Misc{	  dst07,
  author	= {David L. Donoho and V. C. Stodden and Yaakov Tsaig},
  howpublished	= {\url{http://sparselab.stanford.edu/}},
  title		= {Sparselab},
  year		= 2007
}

@TechReport{	  dtds06,
  author	= {David L. Donoho and Yaakov Tsaig and Iddo Drori and
		  Jean-Luc Starck},
  title		= {Sparse Solution of Underdetermined Linear Equations by
		  Stagewise Orthogonal Matching Pursuit},
  institution	= {Dept. of Statistics, Stanford University},
  number	= {2006-2},
  month		= {April},
  url		= {http://www-stat.stanford.edu/~idrori/StOMP.pdf,
		  http://www-stat.stanford.edu/reports/abstracts/06-02.pdf},
  year		= 2006
}

###TechReport{	  dtds06,
  author	= {David L. Donoho and Yaakov Tsaig and Iddo Drori and
		  Jean-Luc Starck},
  title		= {Sparse Solution of Underdetermined Linear Equations by
		  Stagewise Orthogonal Matching Pursuit},
  institution	= {Dept. of Statistics, Stanford University},
  number	= {2006-2},
  month		= {April},
  url		= {http://www-stat.stanford.edu/~idrori/StOMP.pdf,
		  http://www-stat.stanford.edu/reports/abstracts/06-02.pdf},
  year		= 2006
}

###TechReport{	  dtds06,
  author	= {David L. Donoho and Yaakov Tsaig and Iddo Drori and
		  Jean-Luc Starck},
  title		= {Sparse Solution of Underdetermined Linear Equations by
		  Stagewise Orthogonal Matching Pursuit},
  institution	= {Dept. of Statistics, Stanford University},
  number	= {2006-2},
  month		= {April},
  url		= {http://www-stat.stanford.edu/~idrori/StOMP.pdf,
		  http://www-stat.stanford.edu/reports/abstracts/06-02.pdf},
  year		= 2006
}

@Article{	  duange:2006,
  author	= {P.-C Du and Ruth H. Angeletti},
  title		= {Automatic deconvolution of isotope-resolved mass spectra
		  using variable selection and quantized peptide mass
		  distribution},
  journal	= {Analytical Chemistry},
  volume	= 78,
  number	= 10,
  month		= {May},
  year		= 2006,
  pages		= {3385--3392}
}

###Article{	  duange:2006,
  author	= {P.-C Du and Ruth H. Angeletti},
  title		= {Automatic deconvolution of isotope-resolved mass spectra
		  using variable selection and quantized peptide mass
		  distribution},
  journal	= {Analytical Chemistry},
  volume	= 78,
  number	= 10,
  month		= {May},
  year		= 2006,
  pages		= {3385--3392}
}

###Article{	  duange:2006,
  author	= {P.-C Du and Ruth H. Angeletti},
  title		= {Automatic deconvolution of isotope-resolved mass spectra
		  using variable selection and quantized peptide mass
		  distribution},
  journal	= {Analytical Chemistry},
  volume	= 78,
  number	= 10,
  month		= {May},
  year		= 2006,
  pages		= {3385--3392}
}

@InProceedings{	  duchshalsingchan:2008,
  author	= {J. Duchi and S. Shalev-Shwartz and Y. Singer and T.
		  Chandra},
  title		= {Efficient projections onto the $\ell_1$-ball for learning
		  in high dimensions},
  booktitle	= {Proceedings of the 25th International Conference on
		  Machine Learning},
  year		= 2008,
  pages		= {272--279}
}

###InProceedings{ duchshalsingchan:2008,
  author	= {J. Duchi and S. Shalev-Shwartz and Y. Singer and T.
		  Chandra},
  title		= {Efficient projections onto the $\ell_1$-ball for learning
		  in high dimensions},
  booktitle	= {Proceedings of the 25th International Conference on
		  Machine Learning},
  year		= 2008,
  pages		= {272--279}
}

###InProceedings{ duchshalsingchan:2008,
  author	= {J. Duchi and S. Shalev-Shwartz and Y. Singer and T.
		  Chandra},
  title		= {Efficient projections onto the $\ell_1$-ball for learning
		  in high dimensions},
  booktitle	= {Proceedings of the 25th International Conference on
		  Machine Learning},
  year		= 2008,
  pages		= {272--279}
}

@Article{	  duff:2004,
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###Article{	  duff:2004,
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  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= acmmathsoft,
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  title		= {{MA57}---a code for the solution of sparse symmetric
		  definite and indefinite systems},
  volume	= 30,
  year		= 2004
}

###Article{	  duff:2004,
  author	= {Iain S. Duff},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= acmmathsoft,
  number	= 2,
  pages		= {118--144},
  title		= {{MA57}---a code for the solution of sparse symmetric
		  definite and indefinite systems},
  volume	= 30,
  year		= 2004
}

@InCollection{	  dunn:1994,
  author	= {J. C. Dunn},
  booktitle	= {Large Scale Optimization: State of the Art},
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  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {W. W. Hager, D. W. Hearn, and P. M. Pardalos},
  pages		= {95-114},
  publisher	= {Kluwer},
  title		= {Gradient-Related Constrained Minimization Algorithms in
		  Function Spaces: Convergence Properties and Computational
		  Implications},
  year		= 1994
}

###InCollection{  dunn:1994,
  author	= {J. C. Dunn},
  booktitle	= {Large Scale Optimization: State of the Art},
  date-added	= {2007-07-04 23:43:57 -0700},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {W. W. Hager, D. W. Hearn, and P. M. Pardalos},
  pages		= {95-114},
  publisher	= {Kluwer},
  title		= {Gradient-Related Constrained Minimization Algorithms in
		  Function Spaces: Convergence Properties and Computational
		  Implications},
  year		= 1994
}

###InCollection{  dunn:1994,
  author	= {J. C. Dunn},
  booktitle	= {Large Scale Optimization: State of the Art},
  date-added	= {2007-07-04 23:43:57 -0700},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {W. W. Hager, D. W. Hearn, and P. M. Pardalos},
  pages		= {95-114},
  publisher	= {Kluwer},
  title		= {Gradient-Related Constrained Minimization Algorithms in
		  Function Spaces: Convergence Properties and Computational
		  Implications},
  year		= 1994
}

@Article{	  eck93,
  author	= "Jonathan Eckstein",
  title		= "Nonlinear Proximal Point Algorithms Using {Bregman}
		  Functions, with Applications to Convex Programming",
  journal	= mathofor,
  year		= 1993,
  volume	= 18,
  number	= 1,
  page		= {202--226}
}

###Article{	  eck93,
  author	= "Jonathan Eckstein",
  title		= "Nonlinear Proximal Point Algorithms Using {Bregman}
		  Functions, with Applications to Convex Programming",
  journal	= mathofor,
  year		= 1993,
  volume	= 18,
  number	= 1,
  page		= {202--226}
}

###Article{	  eck93,
  author	= "Jonathan Eckstein",
  title		= "Nonlinear Proximal Point Algorithms Using {Bregman}
		  Functions, with Applications to Convex Programming",
  journal	= mathofor,
  year		= 1993,
  volume	= 18,
  number	= 1,
  page		= {202--226}
}

@Article{	  efrohastjohntibs:2004,
  author	= {B. Efron and T. Hastie and I. Johnstone and R.
		  Tibshirani},
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  journal	= {Ann. Statist.},
  local-url	= {file://localhost/Users/mpf/papers/EfroHastJohnTibs04.pdf},
  number	= 2,
  pages		= {407-499},
  title		= {Least angle regression},
  volume	= 32,
  year		= 2004
}

###Article{	  efrohastjohntibs:2004,
  author	= {B. Efron and T. Hastie and I. Johnstone and R.
		  Tibshirani},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Ann. Statist.},
  local-url	= {file://localhost/Users/mpf/papers/EfroHastJohnTibs04.pdf},
  number	= 2,
  pages		= {407-499},
  title		= {Least angle regression},
  volume	= 32,
  year		= 2004
}

###Article{	  efrohastjohntibs:2004,
  author	= {B. Efron and T. Hastie and I. Johnstone and R.
		  Tibshirani},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Ann. Statist.},
  local-url	= {file://localhost/Users/mpf/papers/EfroHastJohnTibs04.pdf},
  number	= 2,
  pages		= {407-499},
  title		= {Least angle regression},
  volume	= 32,
  year		= 2004
}

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  author	= "Eren, T. and Goldenberg, D. K. and Whiteley, W. and Yang,
		  Y. R. and Morse, A. S. and Anderson, B. D. O. and
		  Belhumeur, P.N.",
  title		= "Rigidity, computation, and randomization in network
		  localization",
  year		= 2004,
  booktitle	= "23rd INFOCOM",
  volume	= 4,
  page		= {2673--2684}
}

###InProceedings{ egwymab04,
  author	= "Eren, T. and Goldenberg, D. K. and Whiteley, W. and Yang,
		  Y. R. and Morse, A. S. and Anderson, B. D. O. and
		  Belhumeur, P.N.",
  title		= "Rigidity, computation, and randomization in network
		  localization",
  year		= 2004,
  booktitle	= "23rd INFOCOM",
  volume	= 4,
  page		= {2673--2684}
}

###InProceedings{ egwymab04,
  author	= "Eren, T. and Goldenberg, D. K. and Whiteley, W. and Yang,
		  Y. R. and Morse, A. S. and Anderson, B. D. O. and
		  Belhumeur, P.N.",
  title		= "Rigidity, computation, and randomization in network
		  localization",
  year		= 2004,
  booktitle	= "23rd INFOCOM",
  volume	= 4,
  page		= {2673--2684}
}

@Article{	  el--tapit.tsy.zh:1996,
  author	= {A.~S. {El--Bakry} and R.~A. Tapia and T.~Tsuchiya and
		  Y.~Zhang},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Journal of Optimization Theory and Applications},
  month		= {June},
  pages		= {507--541},
  part		= 3,
  title		= {On the formulation and theory of {Newton} interior-point
		  method for nonlinear programming},
  volume	= 89,
  year		= 1996
}

###Article{	  el--tapit.tsy.zh:1996,
  author	= {A.~S. {El--Bakry} and R.~A. Tapia and T.~Tsuchiya and
		  Y.~Zhang},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Journal of Optimization Theory and Applications},
  month		= {June},
  pages		= {507--541},
  part		= 3,
  title		= {On the formulation and theory of {Newton} interior-point
		  method for nonlinear programming},
  volume	= 89,
  year		= 1996
}

###Article{	  el--tapit.tsy.zh:1996,
  author	= {A.~S. {El--Bakry} and R.~A. Tapia and T.~Tsuchiya and
		  Y.~Zhang},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Journal of Optimization Theory and Applications},
  month		= {June},
  pages		= {507--541},
  part		= 3,
  title		= {On the formulation and theory of {Newton} interior-point
		  method for nonlinear programming},
  volume	= 89,
  year		= 1996
}

@Article{	  eladmilarubi:2007,
  author	= {M. Elad and P. Milanfar and R. Rubinstein},
  title		= {Analysis Versus Synthesis in Signal Priors},
  journal	= {Inverse Problems},
  volume	= 23,
  number	= 3,
  pages		= {947-968},
  month		= {June},
  year		= 2007
}

###Article{	  eladmilarubi:2007,
  author	= {M. Elad and P. Milanfar and R. Rubinstein},
  title		= {Analysis Versus Synthesis in Signal Priors},
  journal	= {Inverse Probl.},
  volume	= 23,
  number	= 3,
  pages		= {947-968},
  month		= {June},
  year		= 2007
}

###Article{	  eladmilarubi:2007,
  author	= {M. Elad and P. Milanfar and R. Rubinstein},
  title		= {Analysis Versus Synthesis in Signal Priors},
  journal	= {Inverse Probl.},
  volume	= 23,
  number	= 3,
  pages		= {947-968},
  month		= {June},
  year		= 2007
}

@Article{	  eldamish:2008,
  author	= {Yonina C. Eldar and Moshe Mishali},
  title		= {Robust Recovery of Signals From a Union of Subspaces},
  journal	= ieeetransinfo,
  doi		= {10.1109/TIT.2009.2030471 },
  volume	= 55,
  year		= 2009,
  abstract	= {Traditional sampling theories consider the problem of
		  reconstructing an unknown signal x from a series of
		  samples. A prevalent assumption which often guarantees
		  recovery from the given measurements is that x lies in a
		  known subspace. Recently, there has been growing interest
		  in nonlinear but structured signal models, in which x lies
		  in a union of subspaces. In this paper, we develop a
		  general framework for robust and efficient recovery of such
		  signals from a given set of samples. More specifically, we
		  treat the case in which x lies in a sum of k subspaces,
		  chosen from a larger set of m possibilities. The samples
		  are modeled as inner products with an arbitrary set of
		  sampling functions. To derive an efficient and robust
		  recovery algorithm, we show that our problem can be
		  formulated as that of recovering a block-sparse vector
		  whose nonzero elements appear in fixed blocks. We then
		  propose a mixed lscr2/lscr1 program for block sparse
		  recovery. Our main result is an equivalence condition under
		  which the proposed convex algorithm is guaranteed to
		  recover the original signal. This result relies on the
		  notion of block restricted isometry property (RIP), which
		  is a generalization of the standard RIP used extensively in
		  the context of compressed sensing. Based on RIP, we also
		  prove stability of our approach in the presence of noise
		  and modeling errors. A special case of our framework is
		  that of recovering multiple measurement vectors (MMV) that
		  share a joint sparsity pattern. Adapting our results to
		  this context leads to new MMV recovery methods as well as
		  equivalence conditions under which the entire set can be
		  determined efficiently. }
}

###Article{	  eldamish:2008,
  author	= {Yonina C. Eldar and Moshe Mishali},
  title		= {Robust Recovery of Signals From a Union of Subspaces},
  journal	= ieeetransinfo,
  doi		= {10.1109/TIT.2009.2030471 },
  volume	= 55,
  year		= 2009,
  abstract	= {Traditional sampling theories consider the problem of
		  reconstructing an unknown signal x from a series of
		  samples. A prevalent assumption which often guarantees
		  recovery from the given measurements is that x lies in a
		  known subspace. Recently, there has been growing interest
		  in nonlinear but structured signal models, in which x lies
		  in a union of subspaces. In this paper, we develop a
		  general framework for robust and efficient recovery of such
		  signals from a given set of samples. More specifically, we
		  treat the case in which x lies in a sum of k subspaces,
		  chosen from a larger set of m possibilities. The samples
		  are modeled as inner products with an arbitrary set of
		  sampling functions. To derive an efficient and robust
		  recovery algorithm, we show that our problem can be
		  formulated as that of recovering a block-sparse vector
		  whose nonzero elements appear in fixed blocks. We then
		  propose a mixed lscr2/lscr1 program for block sparse
		  recovery. Our main result is an equivalence condition under
		  which the proposed convex algorithm is guaranteed to
		  recover the original signal. This result relies on the
		  notion of block restricted isometry property (RIP), which
		  is a generalization of the standard RIP used extensively in
		  the context of compressed sensing. Based on RIP, we also
		  prove stability of our approach in the presence of noise
		  and modeling errors. A special case of our framework is
		  that of recovering multiple measurement vectors (MMV) that
		  share a joint sparsity pattern. Adapting our results to
		  this context leads to new MMV recovery methods as well as
		  equivalence conditions under which the entire set can be
		  determined efficiently. }
}

###Article{	  eldamish:2008,
  author	= {Yonina C. Eldar and Moshe Mishali},
  title		= {Robust Recovery of Signals From a Union of Subspaces},
  journal	= ieeetransinfo,
  doi		= {10.1109/TIT.2009.2030471 },
  volume	= 55,
  year		= 2009,
  abstract	= {Traditional sampling theories consider the problem of
		  reconstructing an unknown signal x from a series of
		  samples. A prevalent assumption which often guarantees
		  recovery from the given measurements is that x lies in a
		  known subspace. Recently, there has been growing interest
		  in nonlinear but structured signal models, in which x lies
		  in a union of subspaces. In this paper, we develop a
		  general framework for robust and efficient recovery of such
		  signals from a given set of samples. More specifically, we
		  treat the case in which x lies in a sum of k subspaces,
		  chosen from a larger set of m possibilities. The samples
		  are modeled as inner products with an arbitrary set of
		  sampling functions. To derive an efficient and robust
		  recovery algorithm, we show that our problem can be
		  formulated as that of recovering a block-sparse vector
		  whose nonzero elements appear in fixed blocks. We then
		  propose a mixed lscr2/lscr1 program for block sparse
		  recovery. Our main result is an equivalence condition under
		  which the proposed convex algorithm is guaranteed to
		  recover the original signal. This result relies on the
		  notion of block restricted isometry property (RIP), which
		  is a generalization of the standard RIP used extensively in
		  the context of compressed sensing. Based on RIP, we also
		  prove stability of our approach in the presence of noise
		  and modeling errors. A special case of our framework is
		  that of recovering multiple measurement vectors (MMV) that
		  share a joint sparsity pattern. Adapting our results to
		  this context leads to new MMV recovery methods as well as
		  equivalence conditions under which the entire set can be
		  determined efficiently. }
}

@Article{	  eldamish:2009,
  author	= {Yonina C. Eldar and Moshe Mishali},
  title		= {Robust Recovery of Signals From a Union of Subspaces},
  year		= 2009,
  doi		= {10.1109/TIT.2009.2030471},
  volume	= 55,
  journal	= ieeetransinfo,
  pages		= {5302-5316}
}

###Article{	  eldamish:2009,
  author	= {Yonina C. Eldar and Moshe Mishali},
  title		= {Robust Recovery of Signals From a Union of Subspaces},
  year		= 2009,
  doi		= {10.1109/TIT.2009.2030471},
  volume	= 55,
  journal	= ieeetransinfo,
  pages		= {5302-5316}
}

###Article{	  eldamish:2009,
  author	= {Yonina C. Eldar and Moshe Mishali},
  title		= {Robust Recovery of Signals From a Union of Subspaces},
  year		= 2009,
  doi		= {10.1109/TIT.2009.2030471},
  volume	= 55,
  journal	= ieeetransinfo,
  pages		= {5302-5316}
}

@Article{	  elde:1990,
  abstract	= {We study a linear, discrete ill-posed problem, by which we
		  mean a very ill-conditioned linear least squares problem.
		  In particular we consider the case when one is primarily
		  interested in computing a functional defined on the
		  solution rather than the solution itself. In order to
		  alleviate the ill-conditioning we require the norm of the
		  solution to be smaller than a given constant. Thus we are
		  lead to minimizing a linear functional subject to two
		  quadratic constraints. We study existence and uniqueness
		  for this problem and show that it is essentially equivalent
		  to a least squares problem with a linear and a quadratic
		  constraint, which is easier to handle computationally.
		  Efficient algorithms are suggested for this problem. },
  address	= {Lawrence, KS, USA},
  author	= {Lars Eld\'{e}n},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= bit,
  keywords	= {ill-posed, least squares, constraint, functional,
		  algorithm},
  number	= 3,
  pages		= {466--483},
  publisher	= {BIT Computer Science and Numerical Mathematics},
  title		= {Algorithms for the Computation of functionals defined on
		  the solution of a discrete ill-posed problem},
  volume	= 30,
  year		= 1990
}

###Article{	  elde:1990,
  abstract	= {We study a linear, discrete ill-posed problem, by which we
		  mean a very ill-conditioned linear least squares problem.
		  In particular we consider the case when one is primarily
		  interested in computing a functional defined on the
		  solution rather than the solution itself. In order to
		  alleviate the ill-conditioning we require the norm of the
		  solution to be smaller than a given constant. Thus we are
		  lead to minimizing a linear functional subject to two
		  quadratic constraints. We study existence and uniqueness
		  for this problem and show that it is essentially equivalent
		  to a least squares problem with a linear and a quadratic
		  constraint, which is easier to handle computationally.
		  Efficient algorithms are suggested for this problem. },
  address	= {Lawrence, KS, USA},
  author	= {Lars Eld\'{e}n},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= bit,
  keywords	= {ill-posed, least squares, constraint, functional,
		  algorithm},
  number	= 3,
  pages		= {466--483},
  publisher	= {BIT Computer Science and Numerical Mathematics},
  title		= {Algorithms for the Computation of functionals defined on
		  the solution of a discrete ill-posed problem},
  volume	= 30,
  year		= 1990
}

###Article{	  elde:1990,
  abstract	= {We study a linear, discrete ill-posed problem, by which we
		  mean a very ill-conditioned linear least squares problem.
		  In particular we consider the case when one is primarily
		  interested in computing a functional defined on the
		  solution rather than the solution itself. In order to
		  alleviate the ill-conditioning we require the norm of the
		  solution to be smaller than a given constant. Thus we are
		  lead to minimizing a linear functional subject to two
		  quadratic constraints. We study existence and uniqueness
		  for this problem and show that it is essentially equivalent
		  to a least squares problem with a linear and a quadratic
		  constraint, which is easier to handle computationally.
		  Efficient algorithms are suggested for this problem. },
  address	= {Lawrence, KS, USA},
  author	= {Lars Eld\'{e}n},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= bit,
  keywords	= {ill-posed, least squares, constraint, functional,
		  algorithm},
  number	= 3,
  pages		= {466--483},
  publisher	= {BIT Computer Science and Numerical Mathematics},
  title		= {Algorithms for the Computation of functionals defined on
		  the solution of a discrete ill-posed problem},
  volume	= 30,
  year		= 1990
}

@Article{	  eldehansroja:2005,
  abstract	= {The minimization of linear functionals defined on the
		  solutions of discrete ill-posed problems arises, e.g., in
		  the computation of confidence intervals for these
		  solutions. In 1990, Eld\'{e}n proposed an algorithm for
		  this minimization problem based on a parametric programming
		  reformulation involving the solution of a sequence of
		  trust-region problems, and using matrix factorizations. In
		  this paper, we describe MLFIP, a largescale version of this
		  algorithm where a limited-memory trust-region solver is
		  used on the subproblems. We illustrate the use of our
		  algorithm in connection with an inverse heat conduction
		  problem. },
  author	= {Lars Eld\'{e}n and Per Christian Hansen and Marielba
		  Rojas},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= bit,
  keywords	= {Discrete ill-posed problems, confidence intervals,
		  large-scale algorithms, trust regions},
  number	= 2,
  pages		= {329--340},
  title		= {Minimization of linear functionals defined on solutions of
		  large-scale discrete-ill posed problems},
  volume	= 45,
  year		= 2005
}

###Article{	  eldehansroja:2005,
  abstract	= {The minimization of linear functionals defined on the
		  solutions of discrete ill-posed problems arises, e.g., in
		  the computation of confidence intervals for these
		  solutions. In 1990, Eld\'{e}n proposed an algorithm for
		  this minimization problem based on a parametric programming
		  reformulation involving the solution of a sequence of
		  trust-region problems, and using matrix factorizations. In
		  this paper, we describe MLFIP, a largescale version of this
		  algorithm where a limited-memory trust-region solver is
		  used on the subproblems. We illustrate the use of our
		  algorithm in connection with an inverse heat conduction
		  problem. },
  author	= {Lars Eld\'{e}n and Per Christian Hansen and Marielba
		  Rojas},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= bit,
  keywords	= {Discrete ill-posed problems, confidence intervals,
		  large-scale algorithms, trust regions},
  number	= 2,
  pages		= {329--340},
  title		= {Minimization of linear functionals defined on solutions of
		  large-scale discrete-ill posed problems},
  volume	= 45,
  year		= 2005
}

###Article{	  eldehansroja:2005,
  abstract	= {The minimization of linear functionals defined on the
		  solutions of discrete ill-posed problems arises, e.g., in
		  the computation of confidence intervals for these
		  solutions. In 1990, Eld\'{e}n proposed an algorithm for
		  this minimization problem based on a parametric programming
		  reformulation involving the solution of a sequence of
		  trust-region problems, and using matrix factorizations. In
		  this paper, we describe MLFIP, a largescale version of this
		  algorithm where a limited-memory trust-region solver is
		  used on the subproblems. We illustrate the use of our
		  algorithm in connection with an inverse heat conduction
		  problem. },
  author	= {Lars Eld\'{e}n and Per Christian Hansen and Marielba
		  Rojas},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= bit,
  keywords	= {Discrete ill-posed problems, confidence intervals,
		  large-scale algorithms, trust regions},
  number	= 2,
  pages		= {329--340},
  title		= {Minimization of linear functionals defined on solutions of
		  large-scale discrete-ill posed problems},
  volume	= 45,
  year		= 2005
}

@Article{	  emiel2010incremental,
  title		= {Incremental-like bundle methods with application to energy
		  planning},
  author	= {Emiel, Gr{\'e}gory and Sagastiz{\'a}bal, Claudia},
  journal	= {Computational Optimization and Applications},
  volume	= {46},
  number	= {2},
  pages		= {305--332},
  year		= {2010},
  publisher	= {Springer}
}

@Article{	  erwaymarcia:2011,
  title		= {A backward stability analysis of diagonal pivoting methods
		  for solving unsymmetric tridiagonal systems without
		  interchanges},
  author	= {Erway, J.B. and Marcia, R.F.},
  journal	= {Numerical Linear Algebra with Applications},
  volume	= 18,
  number	= 1,
  pages		= {41--54},
  year		= 2011,
  publisher	= {Wiley Online Library}
}

###Article{	  erwaymarcia:2011,
  title		= {A backward stability analysis of diagonal pivoting methods
		  for solving unsymmetric tridiagonal systems without
		  interchanges},
  author	= {Erway, J.B. and Marcia, R.F.},
  journal	= {Numerical Linear Algebra with Applications},
  volume	= 18,
  number	= 1,
  pages		= {41--54},
  year		= 2011,
  publisher	= {Wiley Online Library}
}

@Article{	  erwaymarcia:2012,
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  author	= {Erway, J.B. and Marcia, R.F.},
  journal	= {Linear Algebra and its Applications},
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  pages		= {333--334},
  publisher	= {Elsevier}
}

###Article{	  erwaymarcia:2012,
  title		= {Limited-memory {BFGS} systems with diagonal updates},
  author	= {Erway, J.B. and Marcia, R.F.},
  journal	= {Linear Algebra and its Applications},
  year		= 2012,
  pages		= {333--334},
  publisher	= {Elsevier}
}

@PhDThesis{	  esser:2010,
  author	= {John Ernest Esser},
  title		= {Primal Dual Algorithms for Convex Models and Applications
		  to Image Restoration, Registration and Nonlocal
		  Inpainting},
  school	= {University of California, Los Angeles},
  year		= 2010
}

###PhDThesis{	  esser:2010,
  author	= {John Ernest Esser},
  title		= {Primal Dual Algorithms for Convex Models and Applications
		  to Image Restoration, Registration and Nonlocal
		  Inpainting},
  school	= {University of California, Los Angeles},
  year		= 2010
}

@InProceedings{	  evp04,
  author	= "Evgeniou, T. and Pontil, M.",
  title		= "Regularized multi-task learning",
  year		= 2004,
  booktitle	= "10th ACM SIGKDD international conference on Knowledge
		  discovery and data mining",
  page		= {109--117}
}

###InProceedings{ evp04,
  author	= "Evgeniou, T. and Pontil, M.",
  title		= "Regularized multi-task learning",
  year		= 2004,
  booktitle	= "10th ACM SIGKDD international conference on Knowledge
		  discovery and data mining",
  page		= {109--117}
}

###InProceedings{ evp04,
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  title		= "Regularized multi-task learning",
  year		= 2004,
  booktitle	= "10th ACM SIGKDD international conference on Knowledge
		  discovery and data mining",
  page		= {109--117}
}

@Book{		  fap03,
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		  Complementarity Problems",
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  address	= "New York"
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###Book{	  fap03,
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  title		= "Finite-Dimensional Variational Inequalities and
		  Complementarity Problems",
  volume	= "I and II",
  publisher	= "Springer",
  year		= 2003,
  address	= "New York"
}

###Book{	  fap03,
  author	= "Facchinei, F. and Pang, J.-S.",
  title		= "Finite-Dimensional Variational Inequalities and
		  Complementarity Problems",
  volume	= "I and II",
  publisher	= "Springer",
  year		= 2003,
  address	= "New York"
}

@PhDThesis{	  fazel:2002,
  title		= {{Matrix rank minimization with applications}},
  author	= {Fazel, M.},
  school	= {Elec. Eng. Dept, Stanford University},
  year		= 2002
}

###PhDThesis{	  fazel:2002,
  title		= {{Matrix rank minimization with applications}},
  author	= {Fazel, M.},
  school	= {Elec. Eng. Dept, Stanford University},
  year		= 2002
}

###PhDThesis{	  fazel:2002,
  title		= {{Matrix rank minimization with applications}},
  author	= {Fazel, M.},
  school	= {Elec. Eng. Dept, Stanford University},
  year		= 2002
}

@Article{	  fem91,
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###Article{	  fem91,
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  title		= {Finite perturbation of convex programs},
  volume	= 23,
  year		= 1991
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###Article{	  fem91,
  author	= {Ferris, M. C. and Mangasarian, O. L.},
  journal	= {Appl. Math. Optim.},
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  pages		= {263--273},
  title		= {Finite perturbation of convex programs},
  volume	= 23,
  year		= 1991
}

@Article{	  fem94,
  author	= "Ferris, M. C. and Mangasarian, O. L.",
  title		= "Parallel variable distribution",
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  page		= {815--832}
}

###Article{	  fem94,
  author	= "Ferris, M. C. and Mangasarian, O. L.",
  title		= "Parallel variable distribution",
  journal	= siamopt,
  year		= 1994,
  volume	= 4,
  page		= {815--832}
}

###Article{	  fem94,
  author	= "Ferris, M. C. and Mangasarian, O. L.",
  title		= "Parallel variable distribution",
  journal	= siamopt,
  year		= 1994,
  volume	= 4,
  page		= {815--832}
}

@Article{	  fep97,
  author	= {M. C. Ferris and J. S. Pang},
  journal	= siamreview,
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  pages		= {669-713},
  title		= {Engineering and Economic Applications of Complementarity
		  Problems},
  volume	= 39,
  year		= 1997
}

###Article{	  fep97,
  author	= {M. C. Ferris and J. S. Pang},
  journal	= siamreview,
  number	= 4,
  pages		= {669-713},
  title		= {Engineering and Economic Applications of Complementarity
		  Problems},
  volume	= 39,
  year		= 1997
}

###Article{	  fep97,
  author	= {M. C. Ferris and J. S. Pang},
  journal	= siamreview,
  number	= 4,
  pages		= {669-713},
  title		= {Engineering and Economic Applications of Complementarity
		  Problems},
  volume	= 39,
  year		= 1997
}

@Article{	  fesssutt:2003,
  author	= {Jeffrey A. Fessler and Bradley P. Sutton},
  date-modified	= {2007-09-18 22:12:17 -0700},
  journal	= {{IEEE} Trans. Sig. Proc.},
  number	= 2,
  pages		= {560-574},
  title		= {Nonuniform fast Fourier transforms using min-max
		  interpolation},
  url		= {\url{http://www.dsp.rice.edu/software/rwt.shtml}},
  volume	= 51,
  year		= 2003
}

###Article{	  fesssutt:2003,
  author	= {Jeffrey A. Fessler and Bradley P. Sutton},
  date-modified	= {2007-09-18 22:12:17 -0700},
  journal	= {{IEEE} Trans. Sig. Proc.},
  number	= 2,
  pages		= {560-574},
  title		= {Nonuniform fast Fourier transforms using min-max
		  interpolation},
  url		= {\url{http://www.dsp.rice.edu/software/rwt.shtml}},
  volume	= 51,
  year		= 2003
}

###Article{	  fesssutt:2003,
  author	= {Jeffrey A. Fessler and Bradley P. Sutton},
  date-modified	= {2007-09-18 22:12:17 -0700},
  journal	= {{IEEE} Trans. Sig. Proc.},
  number	= 2,
  pages		= {560-574},
  title		= {Nonuniform fast Fourier transforms using min-max
		  interpolation},
  url		= {\url{http://www.dsp.rice.edu/software/rwt.shtml}},
  volume	= 51,
  year		= 2003
}

@Misc{		  fevo:2007,
  author	= {C. F\'{e}votte},
  date-added	= {2007-09-18 21:01:54 -0700},
  date-modified	= {2007-09-18 21:01:54 -0700},
  title		= {{Audio Source Separation Demo Webpage}},
  url		= {http://www.tsi.enst.fr/~fevotte/bass_demo.html},
  year		= 2007
}

###Misc{	  fevo:2007,
  author	= {C. F\'{e}votte},
  date-added	= {2007-09-18 21:01:54 -0700},
  date-modified	= {2007-09-18 21:01:54 -0700},
  title		= {{Audio Source Separation Demo Webpage}},
  url		= {http://www.tsi.enst.fr/~fevotte/bass_demo.html},
  year		= 2007
}

###Misc{	  fevo:2007,
  author	= {C. F\'{e}votte},
  date-added	= {2007-09-18 21:01:54 -0700},
  date-modified	= {2007-09-18 21:01:54 -0700},
  title		= {{Audio Source Separation Demo Webpage}},
  url		= {http://www.tsi.enst.fr/~fevotte/bass_demo.html},
  year		= 2007
}

@InProceedings{	  fhb01,
  author	= "Fazel, M. and Hindi, H. and Boyd, S. P.",
  title		= "A Rank Minimization Heuristic with Application to Minimum
		  Order System Approximation",
  year		= 2001,
  address	= "Arlington",
  booktitle	= "American Control Conference",
  page		= {4734--4739}
}

###InProceedings{ fhb01,
  author	= "Fazel, M. and Hindi, H. and Boyd, S. P.",
  title		= "A Rank Minimization Heuristic with Application to Minimum
		  Order System Approximation",
  year		= 2001,
  address	= "Arlington",
  booktitle	= "American Control Conference",
  page		= {4734--4739}
}

###InProceedings{ fhb01,
  author	= "Fazel, M. and Hindi, H. and Boyd, S. P.",
  title		= "A Rank Minimization Heuristic with Application to Minimum
		  Order System Approximation",
  year		= 2001,
  address	= "Arlington",
  booktitle	= "American Control Conference",
  page		= {4734--4739}
}

@Article{	  fhht07,
  author	= {Friedman, J. and Hastie, T. and H\"{o}fling, H. and
		  Tihshirani, R.},
  title		= "Pathwise coordinate optimization",
  journal	= "Ann. Appl. Stat.",
  year		= 2007,
  volume	= 1,
  page		= {302--332}
}

###Article{	  fhht07,
  author	= {Friedman, J. and Hastie, T. and H\"{o}fling, H. and
		  Tihshirani, R.},
  title		= "Pathwise coordinate optimization",
  journal	= "Ann. Appl. Stat.",
  year		= 2007,
  volume	= 1,
  page		= {302--332}
}

###Article{	  fhht07,
  author	= {Friedman, J. and Hastie, T. and H\"{o}fling, H. and
		  Tihshirani, R.},
  title		= "Pathwise coordinate optimization",
  journal	= "Ann. Appl. Stat.",
  year		= 2007,
  volume	= 1,
  page		= {302--332}
}

@Article{	  fht07,
  author	= "Friedman, J. and Hastie, T. and Tibshirani, R.",
  title		= "Sparse inverse covariance estimation with the graphical
		  lasso",
  journal	= "Biostatistics",
  year		= 2007,
  page		= {1--10}
}

###Article{	  fht07,
  author	= "Friedman, J. and Hastie, T. and Tibshirani, R.",
  title		= "Sparse inverse covariance estimation with the graphical
		  lasso",
  journal	= "Biostatistics",
  year		= 2007,
  page		= {1--10}
}

###Article{	  fht07,
  author	= "Friedman, J. and Hastie, T. and Tibshirani, R.",
  title		= "Sparse inverse covariance estimation with the graphical
		  lasso",
  journal	= "Biostatistics",
  year		= 2007,
  page		= {1--10}
}

@TechReport{	  fht08,
  author	= "Friedman, J. and Hastie, T. and Tihshirani, R.",
  title		= "Regularization paths for generalized linear models via
		  coordinate descent",
  institution	= "Department of Statistics, Stanford University, Stanford",
  type		= {Report},
  year		= 2008,
  month		= "July"
}

###TechReport{	  fht08,
  author	= "Friedman, J. and Hastie, T. and Tihshirani, R.",
  title		= "Regularization paths for generalized linear models via
		  coordinate descent",
  institution	= "Department of Statistics, Stanford University, Stanford",
  type		= {Report},
  year		= 2008,
  month		= "July"
}

###TechReport{	  fht08,
  author	= "Friedman, J. and Hastie, T. and Tihshirani, R.",
  title		= "Regularization paths for generalized linear models via
		  coordinate descent",
  institution	= "Department of Statistics, Stanford University, Stanford",
  type		= {Report},
  year		= 2008,
  month		= "July"
}

@Article{	  fiacliu:1993,
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  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Ann. Oper. Res.},
  pages		= {61-80},
  title		= {Degeneracy in {NLP} and the Development of Results
		  Motivated by its Presence},
  volume	= 46,
  year		= 1993
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###Article{	  fiacliu:1993,
  author	= {Anthony V. Fiacco and Jiming Liu},
  date-modified	= {2007-07-18 14:59:32 -0700},
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  pages		= {61-80},
  title		= {Degeneracy in {NLP} and the Development of Results
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  volume	= 46,
  year		= 1993
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###Article{	  fiacliu:1993,
  author	= {Anthony V. Fiacco and Jiming Liu},
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  journal	= {Ann. Oper. Res.},
  pages		= {61-80},
  title		= {Degeneracy in {NLP} and the Development of Results
		  Motivated by its Presence},
  volume	= 46,
  year		= 1993
}

@Book{		  fiacmcco:1968,
  address	= {New York},
  author	= {Anthony V. Fiacco and Garth P. McCormick},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Wiley},
  title		= {Nonlinear Programming: Sequential Unconstrained
		  Minimization Techniques},
  year		= 1968
}

###Book{	  fiacmcco:1968,
  address	= {New York},
  author	= {Anthony V. Fiacco and Garth P. McCormick},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Wiley},
  title		= {Nonlinear Programming: Sequential Unconstrained
		  Minimization Techniques},
  year		= 1968
}

###Book{	  fiacmcco:1968,
  address	= {New York},
  author	= {Anthony V. Fiacco and Garth P. McCormick},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Wiley},
  title		= {Nonlinear Programming: Sequential Unconstrained
		  Minimization Techniques},
  year		= 1968
}

@Article{	  fignow:2003,
  author	= {M\'ario A. T. Figueiredo and Robert D. Nowak},
  title		= {An {EM} algorithm for wavelet-based image restoration},
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  year		= 2003,
  volume	= 12,
  number	= 8,
  pages		= {906-916},
  month		= {August}
}

###Article{	  fignow:2003,
  author	= {M\'ario A. T. Figueiredo and Robert D. Nowak},
  title		= {An {EM} algorithm for wavelet-based image restoration},
  journal	= ieeetransimproc,
  year		= 2003,
  volume	= 12,
  number	= 8,
  pages		= {906-916},
  month		= {August}
}

###Article{	  fignow:2003,
  author	= {M\'ario A. T. Figueiredo and Robert D. Nowak},
  title		= {An {EM} algorithm for wavelet-based image restoration},
  journal	= ieeetransimproc,
  year		= 2003,
  volume	= 12,
  number	= 8,
  pages		= {906-916},
  month		= {August}
}

@Article{	  figunowawrig:2007,
  title		= {{Gradient Projection for Sparse Reconstruction:
		  Application to Compressed Sensing and Other Inverse
		  Problems}},
  author	= {Figueiredo, M. and Nowak, R. and Wright, S. J.},
  journal	= {Sel. Top. in Signal Process., IEEE J.},
  volume	= 1,
  number	= 4,
  pages		= {586--597},
  year		= 2007
}

###Article{	  figunowawrig:2007,
  title		= {{Gradient Projection for Sparse Reconstruction:
		  Application to Compressed Sensing and Other Inverse
		  Problems}},
  author	= {Figueiredo, M. and Nowak, R. and Wright, S. J.},
  journal	= {Sel. Top. in Signal Process., IEEE J.},
  volume	= 1,
  number	= 4,
  pages		= {586--597},
  year		= 2007
}

###Article{	  figunowawrig:2007,
  title		= {{Gradient Projection for Sparse Reconstruction:
		  Application to Compressed Sensing and Other Inverse
		  Problems}},
  author	= {Figueiredo, M. and Nowak, R. and Wright, S. J.},
  journal	= {Sel. Top. in Signal Process., IEEE J.},
  volume	= 1,
  number	= 4,
  pages		= {586--597},
  year		= 2007
}

@InProceedings{	  fle94,
  author	= "Fletcher, R.",
  title		= "An overview of unconstrained optimization",
  booktitle	= "Algorithms for Continuous Optimization",
  editor	= "E. Spedicato",
  organization	= "Kluwer Academic",
  address	= "Dordrecht",
  year		= 1994,
  page		= {109--143}
}

###InProceedings{ fle94,
  author	= "Fletcher, R.",
  title		= "An overview of unconstrained optimization",
  booktitle	= "Algorithms for Continuous Optimization",
  editor	= "E. Spedicato",
  organization	= "Kluwer Academic",
  address	= "Dordrecht",
  year		= 1994,
  page		= {109--143}
}

###InProceedings{ fle94,
  author	= "Fletcher, R.",
  title		= "An overview of unconstrained optimization",
  booktitle	= "Algorithms for Continuous Optimization",
  editor	= "E. Spedicato",
  organization	= "Kluwer Academic",
  address	= "Dordrecht",
  year		= 1994,
  page		= {109--143}
}

@Article{	  flet:1971,
  abstract	= {An effective algorithm is presented for quadratic
		  programming which is of general applicability, but which is
		  not dependent upon the availability of a linear programming
		  code for its implementation. It is an algorithm of exchange
		  type, the exchanges being chosen so as to avoid the
		  accumulation of error to as large an extent as possible.},
  author	= {R. Fletcher},
  journal	= {J. Institute Math. Appl.},
  pages		= {76--91},
  title		= {A general quadratic programming algorithm},
  volume	= 7,
  year		= 1971
}

###Article{	  flet:1971,
  abstract	= {An effective algorithm is presented for quadratic
		  programming which is of general applicability, but which is
		  not dependent upon the availability of a linear programming
		  code for its implementation. It is an algorithm of exchange
		  type, the exchanges being chosen so as to avoid the
		  accumulation of error to as large an extent as possible.},
  author	= {R. Fletcher},
  journal	= {J. Institute Math. Appl.},
  pages		= {76--91},
  title		= {A general quadratic programming algorithm},
  volume	= 7,
  year		= 1971
}

###Article{	  flet:1971,
  abstract	= {An effective algorithm is presented for quadratic
		  programming which is of general applicability, but which is
		  not dependent upon the availability of a linear programming
		  code for its implementation. It is an algorithm of exchange
		  type, the exchanges being chosen so as to avoid the
		  accumulation of error to as large an extent as possible.},
  author	= {R. Fletcher},
  journal	= {J. Institute Math. Appl.},
  pages		= {76--91},
  title		= {A general quadratic programming algorithm},
  volume	= 7,
  year		= 1971
}

@InCollection{	  flet:1974,
  address	= {London},
  author	= {R. Fletcher},
  booktitle	= {Numerical Methods for Constrained Optimization},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {P. E. Gill and W. Murray},
  pages		= {219--239},
  publisher	= academic,
  title		= {Methods Related to Lagrangian Functions},
  year		= 1974
}

###InCollection{  flet:1974,
  address	= {London},
  author	= {R. Fletcher},
  booktitle	= {Numerical Methods for Constrained Optimization},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {P. E. Gill and W. Murray},
  pages		= {219--239},
  publisher	= academic,
  title		= {Methods Related to Lagrangian Functions},
  year		= 1974
}

###InCollection{  flet:1974,
  address	= {London},
  author	= {R. Fletcher},
  booktitle	= {Numerical Methods for Constrained Optimization},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {P. E. Gill and W. Murray},
  pages		= {219--239},
  publisher	= academic,
  title		= {Methods Related to Lagrangian Functions},
  year		= 1974
}

@InProceedings{	  flet:1985,
  address	= {Philadelphia},
  author	= {R. Fletcher},
  booktitle	= {Numerical Optimization. 1984},
  editor	= {P. T. Boggs and R. H. Byrd and R. B. Schnabel},
  pages		= {26-40},
  publisher	= siampub,
  title		= {An $\ell_1$ penalty method for nonlinear constraints},
  year		= 1985
}

###InProceedings{ flet:1985,
  address	= {Philadelphia},
  author	= {R. Fletcher},
  booktitle	= {Numerical Optimization. 1984},
  editor	= {P. T. Boggs and R. H. Byrd and R. B. Schnabel},
  pages		= {26-40},
  publisher	= siampub,
  title		= {An $\ell_1$ penalty method for nonlinear constraints},
  year		= 1985
}

###InProceedings{ flet:1985,
  address	= {Philadelphia},
  author	= {R. Fletcher},
  booktitle	= {Numerical Optimization. 1984},
  editor	= {P. T. Boggs and R. H. Byrd and R. B. Schnabel},
  pages		= {26-40},
  publisher	= siampub,
  title		= {An $\ell_1$ penalty method for nonlinear constraints},
  year		= 1985
}

@Book{		  flet:1987,
  address	= {Chichester, UK},
  author	= {R. Fletcher},
  date-modified	= {2007-11-09 15:14:02 -0800},
  edition	= {Second},
  publisher	= {Wiley},
  title		= {Practical Methods of Optimization},
  year		= 1987
}

###Book{	  flet:1987,
  address	= {Chichester, UK},
  author	= {R. Fletcher},
  date-modified	= {2007-11-09 15:14:02 -0800},
  edition	= {Second},
  publisher	= {Wiley},
  title		= {Practical Methods of Optimization},
  year		= 1987
}

###Book{	  flet:1987,
  address	= {Chichester, UK},
  author	= {R. Fletcher},
  date-modified	= {2007-11-09 15:14:02 -0800},
  edition	= {Second},
  publisher	= {Wiley},
  title		= {Practical Methods of Optimization},
  year		= 1987
}

@InProceedings{	  flet:1987b,
  author	= {R. Fletcher},
  title		= {Recent developments in linear and quadratic programming},
  editor	= {Iserles, A. and Powell, M. J. D.},
  booktitle	= {State of the Art in Numerical Analysis. Proceedings of the
		  Joint IMA/SIAM Conference},
  publisher	= {Oxford University Press, Oxford, England},
  year		= 1987,
  pages		= {213--243},
  abstract	= {Describes recent developments in linear programming,
		  including the ellipsoid algorithm, the Karmarkar algorithm,
		  new strategies for updating LU factors in the simplex
		  method, and methods with guaranteed termination in the
		  presence of degeneracy and round-off errors. Various new
		  algorithms for quadratic programming are discussed, and the
		  choice of matrix factorizations and their updates is
		  considered. The use of $\ell_1$ penalty functions in linear
		  and quadratic programming is also mentioned briefly.}
}

###InProceedings{ flet:1987b,
  author	= {R. Fletcher},
  title		= {Recent developments in linear and quadratic programming},
  editor	= {Iserles, A. and Powell, M. J. D.},
  booktitle	= {State of the Art in Numerical Analysis. Proceedings of the
		  Joint IMA/SIAM Conference},
  publisher	= {Oxford University Press, Oxford, England},
  year		= 1987,
  pages		= {213--243},
  abstract	= {Describes recent developments in linear programming,
		  including the ellipsoid algorithm, the Karmarkar algorithm,
		  new strategies for updating LU factors in the simplex
		  method, and methods with guaranteed termination in the
		  presence of degeneracy and round-off errors. Various new
		  algorithms for quadratic programming are discussed, and the
		  choice of matrix factorizations and their updates is
		  considered. The use of $\ell_1$ penalty functions in linear
		  and quadratic programming is also mentioned briefly.}
}

###InProceedings{ flet:1987b,
  author	= {R. Fletcher},
  title		= {Recent developments in linear and quadratic programming},
  editor	= {Iserles, A. and Powell, M. J. D.},
  booktitle	= {State of the Art in Numerical Analysis. Proceedings of the
		  Joint IMA/SIAM Conference},
  publisher	= {Oxford University Press, Oxford, England},
  year		= 1987,
  pages		= {213--243},
  abstract	= {Describes recent developments in linear programming,
		  including the ellipsoid algorithm, the Karmarkar algorithm,
		  new strategies for updating LU factors in the simplex
		  method, and methods with guaranteed termination in the
		  presence of degeneracy and round-off errors. Various new
		  algorithms for quadratic programming are discussed, and the
		  choice of matrix factorizations and their updates is
		  considered. The use of $\ell_1$ penalty functions in linear
		  and quadratic programming is also mentioned briefly.}
}

@TechReport{	  flet:1991,
  address	= {Dundee, Scotland},
  author	= {R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {University of Dundee},
  number	= {NA-135},
  title		= {Resolving degeneracy in quadratic programming},
  year		= 1991
}

###TechReport{	  flet:1991,
  address	= {Dundee, Scotland},
  author	= {R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {University of Dundee},
  number	= {NA-135},
  title		= {Resolving degeneracy in quadratic programming},
  year		= 1991
}

###TechReport{	  flet:1991,
  address	= {Dundee, Scotland},
  author	= {R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {University of Dundee},
  number	= {NA-135},
  title		= {Resolving degeneracy in quadratic programming},
  year		= 1991
}

@Article{	  flet:1993,
  abstract	= {A technique for the resolution of degeneracy in an active
		  set method for quadratic programming is described. The
		  approach generalises Fletcher's method (1988) which applies
		  to the LP case. The method is described in terms of a
		  linear complementarity problem tableau, which is seen to
		  provide useful insights. It is shown that the degeneracy
		  procedure only needs to operate when the degenerate
		  constraints are linearly dependent on those in the active
		  set. No significant overheads are incurred by the
		  degeneracy procedure. It is readily implemented in a null
		  space format, and no complications in the matrix algebra
		  are introduced. The guarantees of termination provided by
		  Fletcher's method, extending in particular to the case
		  where round-off error is present, are preserved in the QP
		  case. It is argued that the technique gives stronger
		  guarantees than are available with other popular methods
		  such as Wolfe's method (1963) or the method of Goldfarb and
		  Idnani (1983).},
  author	= {R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Ann. Oper. Res.},
  number	= {1--4},
  pages		= {307--334},
  title		= {Resolving degeneracy in quadratic programming},
  volume	= {46--47},
  year		= 1993
}

###Article{	  flet:1993,
  abstract	= {A technique for the resolution of degeneracy in an active
		  set method for quadratic programming is described. The
		  approach generalises Fletcher's method (1988) which applies
		  to the LP case. The method is described in terms of a
		  linear complementarity problem tableau, which is seen to
		  provide useful insights. It is shown that the degeneracy
		  procedure only needs to operate when the degenerate
		  constraints are linearly dependent on those in the active
		  set. No significant overheads are incurred by the
		  degeneracy procedure. It is readily implemented in a null
		  space format, and no complications in the matrix algebra
		  are introduced. The guarantees of termination provided by
		  Fletcher's method, extending in particular to the case
		  where round-off error is present, are preserved in the QP
		  case. It is argued that the technique gives stronger
		  guarantees than are available with other popular methods
		  such as Wolfe's method (1963) or the method of Goldfarb and
		  Idnani (1983).},
  author	= {R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Ann. Oper. Res.},
  number	= {1--4},
  pages		= {307--334},
  title		= {Resolving degeneracy in quadratic programming},
  volume	= {46--47},
  year		= 1993
}

###Article{	  flet:1993,
  abstract	= {A technique for the resolution of degeneracy in an active
		  set method for quadratic programming is described. The
		  approach generalises Fletcher's method (1988) which applies
		  to the LP case. The method is described in terms of a
		  linear complementarity problem tableau, which is seen to
		  provide useful insights. It is shown that the degeneracy
		  procedure only needs to operate when the degenerate
		  constraints are linearly dependent on those in the active
		  set. No significant overheads are incurred by the
		  degeneracy procedure. It is readily implemented in a null
		  space format, and no complications in the matrix algebra
		  are introduced. The guarantees of termination provided by
		  Fletcher's method, extending in particular to the case
		  where round-off error is present, are preserved in the QP
		  case. It is argued that the technique gives stronger
		  guarantees than are available with other popular methods
		  such as Wolfe's method (1963) or the method of Goldfarb and
		  Idnani (1983).},
  author	= {R. Fletcher},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Ann. Oper. Res.},
  number	= {1--4},
  pages		= {307--334},
  title		= {Resolving degeneracy in quadratic programming},
  volume	= {46--47},
  year		= 1993
}

@InCollection{	  fletcher:1970,
  author	= {R. Fletcher},
  title		= {A class of methods for nonlinear programming with
		  termination and convergence properties},
  booktitle	= {Integer and nonlinear programming},
  editors	= {J. Abadie},
  publisher	= {North-Holland},
  address	= {Amsterdam},
  pages		= {157--173},
  year		= 1970
}

###InCollection{  fletcher:1970,
  author	= {R. Fletcher},
  title		= {A class of methods for nonlinear programming with
		  termination and convergence properties},
  booktitle	= {Integer and nonlinear programming},
  editors	= {J. Abadie},
  publisher	= {North-Holland},
  address	= {Amsterdam},
  pages		= {157--173},
  year		= 1970
}

@InCollection{	  fletcher:1973,
  author	= {R. Fletcher},
  title		= {A Class of Methods for Non-Linear Programming {III}: Rates
		  of Convergence},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  editors	= {F. A. Lootsma},
  publisher	= {Academic Press},
  address	= {NY},
  pages		= {371--393},
  year		= 1973
}

###InCollection{  fletcher:1973,
  author	= {R. Fletcher},
  title		= {A Class of Methods for Non-Linear Programming {III}: Rates
		  of Convergence},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  editors	= {F. A. Lootsma},
  publisher	= {Academic Press},
  address	= {NY},
  pages		= {371--393},
  year		= 1973
}

###InCollection{  fletcher:1973,
  author	= {R. Fletcher},
  title		= {A Class of Methods for Non-Linear Programming {III}: Rates
		  of Convergence},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  editors	= {F. A. Lootsma},
  publisher	= {Academic Press},
  address	= {NY},
  pages		= {371--393},
  year		= 1973
}

@Article{	  fletcher:1973b,
  author	= {R. Fletcher},
  title		= {An exact penalty function for nonlinear programming with
		  inequalities},
  journal	= mathprog,
  year		= 1973,
  volume	= 5,
  pages		= {129--150}
}

###Article{	  fletcher:1973b,
  author	= {R. Fletcher},
  title		= {An exact penalty function for nonlinear programming with
		  inequalities},
  journal	= mathprog,
  year		= 1973,
  volume	= 5,
  pages		= {129--150}
}

###Article{	  fletcher:1973b,
  author	= {R. Fletcher},
  title		= {An exact penalty function for nonlinear programming with
		  inequalities},
  journal	= mathprog,
  year		= 1973,
  volume	= 5,
  pages		= {129--150}
}

@InCollection{	  fletcher:1975,
  author	= {R. Fletcher},
  title		= {An ideal penalty function for constrained optimization},
  booktitle	= {Nonlinear Prog.},
  pages		= {121-163},
  publisher	= {Academic Press},
  year		= 1975,
  editor	= {O. Mangasarian and R. Meyer and S. Robinson},
  volume	= 2
}

###InCollection{  fletcher:1975,
  author	= {R. Fletcher},
  title		= {An ideal penalty function for constrained optimization},
  booktitle	= {Nonlinear Prog.},
  pages		= {121-163},
  publisher	= {Academic Press},
  year		= 1975,
  editor	= {O. Mangasarian and R. Meyer and S. Robinson},
  volume	= 2
}

###InCollection{  fletcher:1975,
  author	= {R. Fletcher},
  title		= {An ideal penalty function for constrained optimization},
  booktitle	= {Nonlinear Prog.},
  pages		= {121-163},
  publisher	= {Academic Press},
  year		= 1975,
  editor	= {O. Mangasarian and R. Meyer and S. Robinson},
  volume	= 2
}

@TechReport{	  fletleyf:1997,
  address	= {Scotland},
  author	= {R. Fletcher and S. Leyffer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Dept. of Mathematics, University of Dundee},
  number	= {Numerical Analysis Report NA/171},
  title		= {Nonlinear programming without a penalty function},
  year		= 1997
}

###TechReport{	  fletleyf:1997,
  address	= {Scotland},
  author	= {R. Fletcher and S. Leyffer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Dept. of Mathematics, University of Dundee},
  number	= {Numerical Analysis Report NA/171},
  title		= {Nonlinear programming without a penalty function},
  year		= 1997
}

###TechReport{	  fletleyf:1997,
  address	= {Scotland},
  author	= {R. Fletcher and S. Leyffer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Dept. of Mathematics, University of Dundee},
  number	= {Numerical Analysis Report NA/171},
  title		= {Nonlinear programming without a penalty function},
  year		= 1997
}

@TechReport{	  fletleyf:1998,
  author	= {R. Fletcher and S. Leyffer},
  institution	= {University of Dundee},
  month		= {March},
  number	= {NA-181},
  title		= {User manual for {FilterSQP}},
  year		= 1999
}

###TechReport{	  fletleyf:1998,
  author	= {R. Fletcher and S. Leyffer},
  institution	= {University of Dundee},
  month		= {March},
  number	= {NA-181},
  title		= {User manual for {FilterSQP}},
  year		= 1999
}

###TechReport{	  fletleyf:1998,
  author	= {R. Fletcher and S. Leyffer},
  institution	= {University of Dundee},
  month		= {March},
  number	= {NA-181},
  title		= {User manual for {FilterSQP}},
  year		= 1999
}

@TechReport{	  fletleyf:2002,
  author	= {R. Fletcher and S. Leyffer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {University of Dundee},
  month		= {August},
  number	= {NA-210},
  title		= {Numerical experience with solving {MPECs} as {NLPs}},
  year		= 2002
}

###TechReport{	  fletleyf:2002,
  author	= {R. Fletcher and S. Leyffer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {University of Dundee},
  month		= {August},
  number	= {NA-210},
  title		= {Numerical experience with solving {MPECs} as {NLPs}},
  year		= 2002
}

###TechReport{	  fletleyf:2002,
  author	= {R. Fletcher and S. Leyffer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {University of Dundee},
  month		= {August},
  number	= {NA-210},
  title		= {Numerical experience with solving {MPECs} as {NLPs}},
  year		= 2002
}

@Article{	  fletleyf:2002a,
  abstract	= {In this paper the solution of nonlinear programming
		  problems by a Sequential Quadratic Programming (SQP)
		  trust--region algorithm is considered. The aim of the
		  present work is to promote global convergence without the
		  need to use a penalty function. Instead, a new concept of a
		  ``filter'' is introduced which allows a step to be accepted
		  if it reduces either the objective function or the
		  constraint violation function. Numerical tests on a wide
		  range of test problems are very encouraging and the new
		  algorithm compares favourably with LANCELOT and an
		  implementation of S$l_1$QP.},
  author	= {R. Fletcher and S. Leyffer},
  journal	= mathprog,
  month		= {January},
  number	= 2,
  pages		= {239--269},
  summary	= {A Sequential Quadratic Programming (SQP) trust-region
		  algorithm for nonlinear programming is considered, which is
		  globally convergent without the need to use a penalty
		  function. Instead, the concept of a ``filter'' is
		  introduced which allows a step to be accepted if it reduces
		  either the objective function or the constraint violation
		  function. Numerical tests on a wide range of test problems
		  are very encouraging and the new algorithm compares
		  favourably with {\sf LANCELOT} and an implementation of
		  S$l_1$QP.},
  title		= {Nonlinear programming without a penalty function},
  volume	= 91,
  year		= 2002
}

###Article{	  fletleyf:2002a,
  abstract	= {In this paper the solution of nonlinear programming
		  problems by a Sequential Quadratic Programming (SQP)
		  trust--region algorithm is considered. The aim of the
		  present work is to promote global convergence without the
		  need to use a penalty function. Instead, a new concept of a
		  ``filter'' is introduced which allows a step to be accepted
		  if it reduces either the objective function or the
		  constraint violation function. Numerical tests on a wide
		  range of test problems are very encouraging and the new
		  algorithm compares favourably with LANCELOT and an
		  implementation of S$l_1$QP.},
  author	= {R. Fletcher and S. Leyffer},
  journal	= mathprog,
  month		= {January},
  number	= 2,
  pages		= {239--269},
  summary	= {A Sequential Quadratic Programming (SQP) trust-region
		  algorithm for nonlinear programming is considered, which is
		  globally convergent without the need to use a penalty
		  function. Instead, the concept of a ``filter'' is
		  introduced which allows a step to be accepted if it reduces
		  either the objective function or the constraint violation
		  function. Numerical tests on a wide range of test problems
		  are very encouraging and the new algorithm compares
		  favourably with {\sf LANCELOT} and an implementation of
		  S$l_1$QP.},
  title		= {Nonlinear programming without a penalty function},
  volume	= 91,
  year		= 2002
}

###Article{	  fletleyf:2002a,
  abstract	= {In this paper the solution of nonlinear programming
		  problems by a Sequential Quadratic Programming (SQP)
		  trust--region algorithm is considered. The aim of the
		  present work is to promote global convergence without the
		  need to use a penalty function. Instead, a new concept of a
		  ``filter'' is introduced which allows a step to be accepted
		  if it reduces either the objective function or the
		  constraint violation function. Numerical tests on a wide
		  range of test problems are very encouraging and the new
		  algorithm compares favourably with LANCELOT and an
		  implementation of S$l_1$QP.},
  author	= {R. Fletcher and S. Leyffer},
  journal	= mathprog,
  month		= {January},
  number	= 2,
  pages		= {239--269},
  summary	= {A Sequential Quadratic Programming (SQP) trust-region
		  algorithm for nonlinear programming is considered, which is
		  globally convergent without the need to use a penalty
		  function. Instead, the concept of a ``filter'' is
		  introduced which allows a step to be accepted if it reduces
		  either the objective function or the constraint violation
		  function. Numerical tests on a wide range of test problems
		  are very encouraging and the new algorithm compares
		  favourably with {\sf LANCELOT} and an implementation of
		  S$l_1$QP.},
  title		= {Nonlinear programming without a penalty function},
  volume	= 91,
  year		= 2002
}

@TechReport{	  fletleyfralpscho:2002,
  author	= {R. Fletcher and S. Leyffer and Danny Ralph and Stefan
		  Scholtes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {University of Dundee},
  month		= {May},
  number	= {NA-209},
  title		= {Local convergence of {SQP} methods for Mathematical
		  Programs with Equilibrium Constraints},
  year		= 2002
}

###TechReport{	  fletleyfralpscho:2002,
  author	= {R. Fletcher and S. Leyffer and Danny Ralph and Stefan
		  Scholtes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {University of Dundee},
  month		= {May},
  number	= {NA-209},
  title		= {Local convergence of {SQP} methods for Mathematical
		  Programs with Equilibrium Constraints},
  year		= 2002
}

###TechReport{	  fletleyfralpscho:2002,
  author	= {R. Fletcher and S. Leyffer and Danny Ralph and Stefan
		  Scholtes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {University of Dundee},
  month		= {May},
  number	= {NA-209},
  title		= {Local convergence of {SQP} methods for Mathematical
		  Programs with Equilibrium Constraints},
  year		= 2002
}

@TechReport{	  fletleyftoin:1998,
  address	= {Dundee DD1 4HN, Scotland, UK},
  author	= {R. Fletcher and S. Leyffer and \mbox{Ph}. Toint},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Department of Mathematics, University of Dundee},
  month		= {August},
  note		= {Revised October 1999},
  number	= {NA/183},
  title		= {On the global convergence of an {SLP}-filter algorithm},
  type		= {Numerical Analysis Report},
  year		= 1998
}

###TechReport{	  fletleyftoin:1998,
  address	= {Dundee DD1 4HN, Scotland, UK},
  author	= {R. Fletcher and S. Leyffer and \mbox{Ph}. Toint},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Department of Mathematics, University of Dundee},
  month		= {August},
  note		= {Revised October 1999},
  number	= {NA/183},
  title		= {On the global convergence of an {SLP}-filter algorithm},
  type		= {Numerical Analysis Report},
  year		= 1998
}

###TechReport{	  fletleyftoin:1998,
  address	= {Dundee DD1 4HN, Scotland, UK},
  author	= {R. Fletcher and S. Leyffer and \mbox{Ph}. Toint},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Department of Mathematics, University of Dundee},
  month		= {August},
  note		= {Revised October 1999},
  number	= {NA/183},
  title		= {On the global convergence of an {SLP}-filter algorithm},
  type		= {Numerical Analysis Report},
  year		= 1998
}

@Article{	  fletleyftoin:2002,
  author	= {R. Fletcher and S. Leyffer and Ph. Toint},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 13,
  pages		= {44--59},
  title		= {On the Global Convergence of a Filter-{SQP} Algorithm},
  year		= 2002
}

###Article{	  fletleyftoin:2002,
  author	= {R. Fletcher and S. Leyffer and Ph. Toint},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 13,
  pages		= {44--59},
  title		= {On the Global Convergence of a Filter-{SQP} Algorithm},
  year		= 2002
}

###Article{	  fletleyftoin:2002,
  author	= {R. Fletcher and S. Leyffer and Ph. Toint},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 13,
  pages		= {44--59},
  title		= {On the Global Convergence of a Filter-{SQP} Algorithm},
  year		= 2002
}

@Article{	  fletmaza:1989,
  author	= {R. Fletcher and E. \mbox{Sainz} de la Maza},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  number	= 3,
  pages		= {235--256},
  title		= {Nonlinear programming and non-smooth optimization by
		  succssive linear programming},
  volume	= 43,
  year		= 1989
}

###Article{	  fletmaza:1989,
  author	= {R. Fletcher and E. \mbox{Sainz} de la Maza},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  number	= 3,
  pages		= {235--256},
  title		= {Nonlinear programming and non-smooth optimization by
		  succssive linear programming},
  volume	= 43,
  year		= 1989
}

###Article{	  fletmaza:1989,
  author	= {R. Fletcher and E. \mbox{Sainz} de la Maza},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  number	= 3,
  pages		= {235--256},
  title		= {Nonlinear programming and non-smooth optimization by
		  succssive linear programming},
  volume	= 43,
  year		= 1989
}

@TechReport{	  flieheermolz:2006,
  author	= {J. Fliege and C. Heermann and D. Molz},
  date-added	= {2007-10-12 18:17:53 -0700},
  date-modified	= {2007-10-19 18:39:05 -0700},
  institution	= {School of Mathematics, Univ. of Birmingham},
  number	= {2006/34},
  title		= {An adaptive primal-dual warm-start technique for quadratic
		  multiobjective optimization},
  type		= {Tech. rep.},
  year		= 2006
}

###TechReport{	  flieheermolz:2006,
  author	= {J. Fliege and C. Heermann and D. Molz},
  date-added	= {2007-10-12 18:17:53 -0700},
  date-modified	= {2007-10-19 18:39:05 -0700},
  institution	= {School of Mathematics, Univ. of Birmingham},
  number	= {2006/34},
  title		= {An adaptive primal-dual warm-start technique for quadratic
		  multiobjective optimization},
  type		= {Tech. rep.},
  year		= 2006
}

###TechReport{	  flieheermolz:2006,
  author	= {J. Fliege and C. Heermann and D. Molz},
  date-added	= {2007-10-12 18:17:53 -0700},
  date-modified	= {2007-10-19 18:39:05 -0700},
  institution	= {School of Mathematics, Univ. of Birmingham},
  number	= {2006/34},
  title		= {An adaptive primal-dual warm-start technique for quadratic
		  multiobjective optimization},
  type		= {Tech. rep.},
  year		= 2006
}

@Misc{		  fornrauh:2007,
  title		= {Recovery algorithms for vector valued data with joint
		  sparsity constraints},
  author	= {Fornasier, M. and Rauhut, H.},
  journal	= {Arxiv preprint math/0608124},
  year		= 2006,
  note		= {To appear in {\it SIAM J. Numer. Anal.}}
}

###Misc{	  fornrauh:2007,
  title		= {Recovery algorithms for vector valued data with joint
		  sparsity constraints},
  author	= {Fornasier, M. and Rauhut, H.},
  journal	= {Arxiv preprint math/0608124},
  year		= 2006,
  note		= {To appear in {\it SIAM J. Numer. Anal.}}
}

###Misc{	  fornrauh:2007,
  title		= {Recovery algorithms for vector valued data with joint
		  sparsity constraints},
  author	= {Fornasier, M. and Rauhut, H.},
  journal	= {Arxiv preprint math/0608124},
  year		= 2006,
  note		= {To appear in {\it SIAM J. Numer. Anal.}}
}

@Article{	  fors:2002,
  author	= {Forsgren, Anders},
  coden		= {ANMAEL},
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@MastersThesis{	  garr:2008,
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###MastersThesis{ garr:2008,
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  month		= {August},
  school	= {Dept. Computer Science, University of British Columbia},
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  year		= 2008
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###MastersThesis{ garr:2008,
  address	= {Vancouver},
  author	= {M. F. Garrido},
  month		= {August},
  school	= {Dept. Computer Science, University of British Columbia},
  title		= {An all-at-once approach for computing nonnegative tensor
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  year		= 2008
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  booktitle	= {Computational Differentiation: Techniques, Applications,
		  and Tools},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {M. Berz and C. Bischof and G. Corliss and A. Griewank},
  pages		= {173--184},
  publisher	= siampub,
  title		= {More {AD} of Nonlinear {AMPL} Models: Computing {H}essian
		  Information and Exploiting Partial Separability},
  year		= 1996
}

###InCollection{  gay:1996,
  address	= {Philadelphia},
  author	= {David M. Gay},
  booktitle	= {Computational Differentiation: Techniques, Applications,
		  and Tools},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {M. Berz and C. Bischof and G. Corliss and A. Griewank},
  pages		= {173--184},
  publisher	= siampub,
  title		= {More {AD} of Nonlinear {AMPL} Models: Computing {H}essian
		  Information and Exploiting Partial Separability},
  year		= 1996
}

###InCollection{  gay:1996,
  address	= {Philadelphia},
  author	= {David M. Gay},
  booktitle	= {Computational Differentiation: Techniques, Applications,
		  and Tools},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {M. Berz and C. Bischof and G. Corliss and A. Griewank},
  pages		= {173--184},
  publisher	= siampub,
  title		= {More {AD} of Nonlinear {AMPL} Models: Computing {H}essian
		  Information and Exploiting Partial Separability},
  year		= 1996
}

@TechReport{	  gay:1997,
  address	= {Murray Hill, NJ},
  author	= {David M. Gay},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Computing Sciences Research Center, Bell Laboratories},
  month		= {April},
  number	= {97-4-06},
  title		= {Hooking Your Solver to {AMPL}},
  year		= 1997
}

###TechReport{	  gay:1997,
  address	= {Murray Hill, NJ},
  author	= {David M. Gay},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Computing Sciences Research Center, Bell Laboratories},
  month		= {April},
  number	= {97-4-06},
  title		= {Hooking Your Solver to {AMPL}},
  year		= 1997
}

###TechReport{	  gay:1997,
  address	= {Murray Hill, NJ},
  author	= {David M. Gay},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {Computing Sciences Research Center, Bell Laboratories},
  month		= {April},
  number	= {97-4-06},
  title		= {Hooking Your Solver to {AMPL}},
  year		= 1997
}

@TechReport{	  ghmsw86,
  address	= {Stanford, CA},
  author	= {P.E. Gill and S.J. Hammarling and W.Murray and M.A.
		  Saunders and M.H. Wright},
  institution	= {Stanford University},
  number	= {SOL86-1},
  title		= {User's Guide for {LSSOL} ({Version} 1.0): {A} {Fortran}
		  package for constrained linear least-squares and convex
		  quadratic programming},
  year		= 1986
}

###TechReport{	  ghmsw86,
  address	= {Stanford, CA},
  author	= {P.E. Gill and S.J. Hammarling and W.Murray and M.A.
		  Saunders and M.H. Wright},
  institution	= {Stanford University},
  number	= {SOL86-1},
  title		= {User's Guide for {LSSOL} ({Version} 1.0): {A} {Fortran}
		  package for constrained linear least-squares and convex
		  quadratic programming},
  year		= 1986
}

###TechReport{	  ghmsw86,
  address	= {Stanford, CA},
  author	= {P.E. Gill and S.J. Hammarling and W.Murray and M.A.
		  Saunders and M.H. Wright},
  institution	= {Stanford University},
  number	= {SOL86-1},
  title		= {User's Guide for {LSSOL} ({Version} 1.0): {A} {Fortran}
		  package for constrained linear least-squares and convex
		  quadratic programming},
  year		= 1986
}

@Article{	  gilbl89,
  author	= {Jean Charles Gilbert and Claude Lemar\`echal},
  title		= {Some numerical experiments with variable-storage
		  quasi-newton algorithms},
  journal	= mathprog,
  year		= 1989,
  number	= 45,
  pages		= {407--435}
}

###Article{	  gilbl89,
  author	= {Jean Charles Gilbert and Claude Lemar\`echal},
  title		= {Some numerical experiments with variable-storage
		  quasi-{Newton} algorithms},
  journal	= mathprog,
  year		= 1989,
  number	= 45,
  pages		= {407--435}
}

###Article{	  gilbl89,
  author	= {Jean Charles Gilbert and Claude Lemar\`echal},
  title		= {Some numerical experiments with variable-storage
		  quasi-{Newton} algorithms},
  journal	= mathprog,
  year		= 1989,
  number	= 45,
  pages		= {407--435}
}

@Article{	  gillkroy:2004,
  author	= {P. E. Gill and Julia Kroyan},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {1-20},
  title		= {Trust-search methods for unconstrained optimization},
  volume	= 12,
  year		= 2004
}

###Article{	  gillkroy:2004,
  author	= {P. E. Gill and Julia Kroyan},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {1-20},
  title		= {Trust-search methods for unconstrained optimization},
  volume	= 12,
  year		= 2004
}

###Article{	  gillkroy:2004,
  author	= {P. E. Gill and Julia Kroyan},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {1-20},
  title		= {Trust-search methods for unconstrained optimization},
  volume	= 12,
  year		= 2004
}

@Article{	  gillmurr:1974,
  author	= {P. E. Gill and W. Murray},
  journal	= mathprog,
  pages		= {311--350},
  title		= {Newton-type methods for unconstrained and linearly
		  constrained optimization},
  volume	= 29,
  year		= 1974
}

###Article{	  gillmurr:1974,
  author	= {P. E. Gill and W. Murray},
  journal	= mathprog,
  pages		= {311--350},
  title		= {{Newton}-type methods for unconstrained and linearly
		  constrained optimization},
  volume	= 29,
  year		= 1974
}

###Article{	  gillmurr:1974,
  author	= {P. E. Gill and W. Murray},
  journal	= mathprog,
  pages		= {311--350},
  title		= {Newton-type methods for unconstrained and linearly
		  constrained optimization},
  volume	= 29,
  year		= 1974
}

@InCollection{	  gillmurrpick:1977,
  address	= {Wilkinson House, Jordan Hill Road, Oxford, England},
  author	= {P. E. Gill and W. Murray and Susan M. Picken},
  booktitle	= {The {NAG} {Fortran} Library Manual {Mark 6}},
  chapter	= {E04},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {{Numerical Algorithms Group Limited}},
  note		= {Withdrawn March 13, 1988},
  pages		= {170--220},
  publisher	= {Numerical Algorithms Group Limited},
  title		= {{E04UAF}, {E04VAF}, {E04VBF}, {E04WAF}, constrained
		  optimization using sequential augmented {Lagrangian}
		  methods},
  year		= 1977
}

###InCollection{  gillmurrpick:1977,
  address	= {Wilkinson House, Jordan Hill Road, Oxford, England},
  author	= {P. E. Gill and W. Murray and Susan M. Picken},
  booktitle	= {The {NAG} {Fortran} Library Manual {Mark 6}},
  chapter	= {E04},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {{Numerical Algorithms Group Limited}},
  note		= {Withdrawn March 13, 1988},
  pages		= {170--220},
  publisher	= {Numerical Algorithms Group Limited},
  title		= {{E04UAF}, {E04VAF}, {E04VBF}, {E04WAF}, constrained
		  optimization using sequential augmented {Lagrangian}
		  methods},
  year		= 1977
}

###InCollection{  gillmurrpick:1977,
  address	= {Wilkinson House, Jordan Hill Road, Oxford, England},
  author	= {P. E. Gill and W. Murray and Susan M. Picken},
  booktitle	= {The {NAG} {Fortran} Library Manual {Mark 6}},
  chapter	= {E04},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {{Numerical Algorithms Group Limited}},
  note		= {Withdrawn March 13, 1988},
  pages		= {170--220},
  publisher	= {Numerical Algorithms Group Limited},
  title		= {{E04UAF}, {E04VAF}, {E04VBF}, {E04WAF}, constrained
		  optimization using sequential augmented {Lagrangian}
		  methods},
  year		= 1977
}

@Article{	  gillmurrponcsaun:1992,
  address	= {Philadelphia, PA, USA},
  author	= {P. E. Gill and W. Murray and D. B. Poncele\'{o}n and M. A.
		  Saunders},
  doi		= {http://dx.doi.org/10.1137/0613022},
  issn		= {0895-4798},
  journal	= siammatrix,
  number	= 1,
  pages		= {292--311},
  publisher	= {SIAM},
  title		= {Preconditioners for indefinite systems arising in
		  optimization},
  volume	= 13,
  year		= 1992
}

###Article{	  gillmurrponcsaun:1992,
  address	= {Philadelphia, PA, USA},
  author	= {P. E. Gill and W. Murray and D. B. Poncele\'{o}n and M. A.
		  Saunders},
  doi		= {http://dx.doi.org/10.1137/0613022},
  journal	= siammatrix,
  number	= 1,
  pages		= {292--311},
  publisher	= {SIAM},
  title		= {Preconditioners for indefinite systems arising in
		  optimization},
  volume	= 13,
  year		= 1992
}

###Article{	  gillmurrponcsaun:1992,
  address	= {Philadelphia, PA, USA},
  author	= {P. E. Gill and W. Murray and D. B. Poncele\'{o}n and M. A.
		  Saunders},
  doi		= {http://dx.doi.org/10.1137/0613022},
  journal	= siammatrix,
  number	= 1,
  pages		= {292--311},
  publisher	= {SIAM},
  title		= {Preconditioners for indefinite systems arising in
		  optimization},
  volume	= 13,
  year		= 1992
}

@TechReport{	  gillmurrsaun:1995,
  address	= {Stanford University, Stanford},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  institution	= {Department of Operations Research},
  number	= {SOL 95-4},
  title		= {User's Guide for {QPOPT} 1.0: A {F}ortran package for
		  quadratic programming},
  type		= {Tech. rep.},
  year		= 1995
}

###TechReport{	  gillmurrsaun:1995,
  address	= {Stanford University, Stanford},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  institution	= {Department of Operations Research},
  number	= {SOL 95-4},
  title		= {User's Guide for {QPOPT} 1.0: A {F}ortran package for
		  quadratic programming},
  type		= {Tech. rep.},
  year		= 1995
}

###TechReport{	  gillmurrsaun:1995,
  address	= {Stanford University, Stanford},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  institution	= {Department of Operations Research},
  number	= {SOL 95-4},
  title		= {User's Guide for {QPOPT} 1.0: A {F}ortran package for
		  quadratic programming},
  type		= {Tech. rep.},
  year		= 1995
}

@TechReport{	  gillmurrsaun:1997,
  address	= {Department of Operations Research, Stanford University,
		  Stanford, CA},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  institution	= {Systems Optimization Laboratory},
  month		= {May},
  number	= {98-1},
  title		= {User's Guide for {SNOPT 5.3}: A {Fortran} Package for
		  Large-scale Nonlinear Programming},
  type		= {Tech. rep.},
  year		= 1997
}

###TechReport{	  gillmurrsaun:1997,
  address	= {Department of Operations Research, Stanford University,
		  Stanford, CA},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  institution	= {Systems Optimization Laboratory},
  month		= {May},
  number	= {98-1},
  title		= {User's Guide for {SNOPT 5.3}: A {Fortran} Package for
		  Large-scale Nonlinear Programming},
  type		= {Tech. rep.},
  year		= 1997
}

###TechReport{	  gillmurrsaun:1997,
  address	= {Department of Operations Research, Stanford University,
		  Stanford, CA},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  institution	= {Systems Optimization Laboratory},
  month		= {May},
  number	= {98-1},
  title		= {User's Guide for {SNOPT 5.3}: A {Fortran} Package for
		  Large-scale Nonlinear Programming},
  type		= {Tech. rep.},
  year		= 1997
}

@TechReport{	  gillmurrsaun:1997a,
  address	= {University of California, San Diego},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Mathematics},
  number	= {Report NA 97-4},
  title		= {User's Guide for {SQOPT} 5.3: a {F}ortran Package for
		  Large-Scale Linear and Quadratic Programming},
  type		= {Technical Report},
  year		= 1997
}

###TechReport{	  gillmurrsaun:1997a,
  address	= {University of California, San Diego},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Mathematics},
  number	= {Report NA 97-4},
  title		= {User's Guide for {SQOPT} 5.3: a {F}ortran Package for
		  Large-Scale Linear and Quadratic Programming},
  type		= {Technical Report},
  year		= 1997
}

###TechReport{	  gillmurrsaun:1997a,
  address	= {University of California, San Diego},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Mathematics},
  number	= {Report NA 97-4},
  title		= {User's Guide for {SQOPT} 5.3: a {F}ortran Package for
		  Large-Scale Linear and Quadratic Programming},
  type		= {Technical Report},
  year		= 1997
}

@TechReport{	  gillmurrsaun:1997b,
  address	= {Department of Operations Research, Stanford University,
		  Stanford, CA},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Systems Optimization Laboratory, Stanford University},
  month		= {August},
  number	= {97-3},
  title		= {{SNOPT}: An {SQP} Algorithm for Large-Scale Constrained
		  Optimization},
  year		= 1997
}

###TechReport{	  gillmurrsaun:1997b,
  address	= {Department of Operations Research, Stanford University,
		  Stanford, CA},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Systems Optimization Laboratory, Stanford University},
  month		= {August},
  number	= {97-3},
  title		= {{SNOPT}: An {SQP} Algorithm for Large-Scale Constrained
		  Optimization},
  year		= 1997
}

###TechReport{	  gillmurrsaun:1997b,
  address	= {Department of Operations Research, Stanford University,
		  Stanford, CA},
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Systems Optimization Laboratory, Stanford University},
  month		= {August},
  number	= {97-3},
  title		= {{SNOPT}: An {SQP} Algorithm for Large-Scale Constrained
		  Optimization},
  year		= 1997
}

@Article{	  gillmurrsaun:2002,
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {979--1006},
  title		= {{SNOPT}: An {SQP} algorithm for large-scale constrained
		  optimization},
  volume	= 12,
  year		= 2002
}

###Article{	  gillmurrsaun:2002,
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {979--1006},
  title		= {{SNOPT}: An {SQP} algorithm for large-scale constrained
		  optimization},
  volume	= 12,
  year		= 2002
}

###Article{	  gillmurrsaun:2002,
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {979--1006},
  title		= {{SNOPT}: An {SQP} algorithm for large-scale constrained
		  optimization},
  volume	= 12,
  year		= 2002
}

@Article{	  gillmurrsaun:2005,
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {99--131},
  title		= {{SNOPT}: An {SQP} algorithm for large-scale constrained
		  optimization},
  volume	= 47,
  year		= 2005
}

###Article{	  gillmurrsaun:2005,
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {99--131},
  title		= {{SNOPT}: An {SQP} algorithm for large-scale constrained
		  optimization},
  volume	= 47,
  year		= 2005
}

###Article{	  gillmurrsaun:2005,
  author	= {P. E. Gill and W. Murray and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {99--131},
  title		= {{SNOPT}: An {SQP} algorithm for large-scale constrained
		  optimization},
  volume	= 47,
  year		= 2005
}

@Article{	  gillmurrsaunwrig:1989,
  title		= {A practical anti-cycling procedure for linearly
		  constrained optimization},
  author	= {Gill, P.E. and Murray, W. and Saunders, M.A. and Wright,
		  M.H.},
  journal	= mathprog,
  volume	= 45,
  number	= 1,
  pages		= {437--474},
  year		= 1989
}

###Article{	  gillmurrsaunwrig:1989,
  title		= {A practical anti-cycling procedure for linearly
		  constrained optimization},
  author	= {Gill, P.E. and Murray, W. and Saunders, M.A. and Wright,
		  M.H.},
  journal	= mathprog,
  volume	= 45,
  number	= 1,
  pages		= {437--474},
  year		= 1989
}

###Article{	  gillmurrsaunwrig:1989,
  title		= {A practical anti-cycling procedure for linearly
		  constrained optimization},
  author	= {Gill, P.E. and Murray, W. and Saunders, M.A. and Wright,
		  M.H.},
  journal	= mathprog,
  volume	= 45,
  number	= 1,
  pages		= {437--474},
  year		= 1989
}

@InProceedings{	  gillmurrsaunwrig:1990,
  author	= {P. E. Gill and W. Murray and M. A. Saunders and Margaret
		  H. Wright},
  booktitle	= {Reliable Numerical Computation},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {M. G. Cox and S. J. Hammarling},
  pages		= {113--138},
  publisher	= oxfordpress,
  title		= {A {Schur}-Complement Method for Sparse Quadratic
		  Programming},
  year		= 1990
}

###InProceedings{ gillmurrsaunwrig:1990,
  author	= {P. E. Gill and W. Murray and M. A. Saunders and Margaret
		  H. Wright},
  booktitle	= {Reliable Numerical Computation},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {M. G. Cox and S. J. Hammarling},
  pages		= {113--138},
  publisher	= oxfordpress,
  title		= {A {Schur}-Complement Method for Sparse Quadratic
		  Programming},
  year		= 1990
}

###InProceedings{ gillmurrsaunwrig:1990,
  author	= {P. E. Gill and W. Murray and M. A. Saunders and Margaret
		  H. Wright},
  booktitle	= {Reliable Numerical Computation},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {M. G. Cox and S. J. Hammarling},
  pages		= {113--138},
  publisher	= oxfordpress,
  title		= {A {Schur}-Complement Method for Sparse Quadratic
		  Programming},
  year		= 1990
}

@Article{	  gillmurrsaunwrig:1991,
  abstract	= {Active-set quadratic programming (QP) methods use a
		  working set to define the search direction and multiplier
		  estimates. In the method proposed by Fletcher in 1971, and
		  in several subsequent mathematically equivalent methods,
		  the working set is chosen to control the inertia of the
		  reduced Hessian, which is never permitted to have more than
		  one nonpositive eigenvalue. (Such methods will be called
		  inertia-controlling.) This paper presents an overview of a
		  generic inertia-controlling QP method, including the
		  equations satisfied by the search direction when the
		  reduced Hessian is positive definite, singular and
		  indefinite. Recurrence relations are derived that define
		  the search direction and Lagrange multiplier vector through
		  equations related to the Karush-Kuhn-Tucker system.
		  Discussion is included of connections with
		  inertia-controlling methods that maintain an explicit
		  factorization of the reduced Hessian matrix.},
  author	= {P. E. Gill and W. Murray and M. A. Saunders and M. H.
		  Wright},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {1--36},
  title		= {Inertia-controlling methods for general quadratic
		  programming},
  volume	= 33,
  year		= 1991
}

###Article{	  gillmurrsaunwrig:1991,
  abstract	= {Active-set quadratic programming (QP) methods use a
		  working set to define the search direction and multiplier
		  estimates. In the method proposed by Fletcher in 1971, and
		  in several subsequent mathematically equivalent methods,
		  the working set is chosen to control the inertia of the
		  reduced Hessian, which is never permitted to have more than
		  one nonpositive eigenvalue. (Such methods will be called
		  inertia-controlling.) This paper presents an overview of a
		  generic inertia-controlling QP method, including the
		  equations satisfied by the search direction when the
		  reduced Hessian is positive definite, singular and
		  indefinite. Recurrence relations are derived that define
		  the search direction and Lagrange multiplier vector through
		  equations related to the Karush-Kuhn-Tucker system.
		  Discussion is included of connections with
		  inertia-controlling methods that maintain an explicit
		  factorization of the reduced Hessian matrix.},
  author	= {P. E. Gill and W. Murray and M. A. Saunders and M. H.
		  Wright},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {1--36},
  title		= {Inertia-controlling methods for general quadratic
		  programming},
  volume	= 33,
  year		= 1991
}

###Article{	  gillmurrsaunwrig:1991,
  abstract	= {Active-set quadratic programming (QP) methods use a
		  working set to define the search direction and multiplier
		  estimates. In the method proposed by Fletcher in 1971, and
		  in several subsequent mathematically equivalent methods,
		  the working set is chosen to control the inertia of the
		  reduced Hessian, which is never permitted to have more than
		  one nonpositive eigenvalue. (Such methods will be called
		  inertia-controlling.) This paper presents an overview of a
		  generic inertia-controlling QP method, including the
		  equations satisfied by the search direction when the
		  reduced Hessian is positive definite, singular and
		  indefinite. Recurrence relations are derived that define
		  the search direction and Lagrange multiplier vector through
		  equations related to the Karush-Kuhn-Tucker system.
		  Discussion is included of connections with
		  inertia-controlling methods that maintain an explicit
		  factorization of the reduced Hessian matrix.},
  author	= {P. E. Gill and W. Murray and M. A. Saunders and M. H.
		  Wright},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  number	= 1,
  pages		= {1--36},
  title		= {Inertia-controlling methods for general quadratic
		  programming},
  volume	= 33,
  year		= 1991
}

@InCollection{	  gillmurrsaunwrig:1992,
  author	= {P. E. Gill and W. Murray and M. A. Saunders and Margaret
		  H. Wright},
  booktitle	= {Advances in Optimization and Parallel Computing},
  editor	= {P. M. Pardalos},
  pages		= {101-128},
  publisher	= {Elsevier},
  title		= {Some Theoreical Properties of an augmented {L}agrangian
		  Merit Function},
  year		= 1992
}

###InCollection{  gillmurrsaunwrig:1992,
  author	= {P. E. Gill and W. Murray and M. A. Saunders and Margaret
		  H. Wright},
  booktitle	= {Advances in Optimization and Parallel Computing},
  editor	= {P. M. Pardalos},
  pages		= {101-128},
  publisher	= {Elsevier},
  title		= {Some Theoreical Properties of an augmented {L}agrangian
		  Merit Function},
  year		= 1992
}

###InCollection{  gillmurrsaunwrig:1992,
  author	= {P. E. Gill and W. Murray and M. A. Saunders and Margaret
		  H. Wright},
  booktitle	= {Advances in Optimization and Parallel Computing},
  editor	= {P. M. Pardalos},
  pages		= {101-128},
  publisher	= {Elsevier},
  title		= {Some Theoreical Properties of an augmented {L}agrangian
		  Merit Function},
  year		= 1992
}

@Book{		  gillmurrwrig:1981,
  address	= {London},
  author	= {P. E. Gill and W. Murray and Margaret H. Wright},
  date-modified	= {2007-07-18 14:59:33 -0700},
  pages		= {xvi+401},
  publisher	= academic,
  title		= {Practical Optimization},
  year		= 1981
}

###Book{	  gillmurrwrig:1981,
  address	= {London},
  author	= {P. E. Gill and W. Murray and Margaret H. Wright},
  date-modified	= {2007-07-18 14:59:33 -0700},
  pages		= {xvi+401},
  publisher	= academic,
  title		= {Practical Optimization},
  year		= 1981
}

###Book{	  gillmurrwrig:1981,
  address	= {London},
  author	= {P. E. Gill and W. Murray and Margaret H. Wright},
  date-modified	= {2007-07-18 14:59:33 -0700},
  pages		= {xvi+401},
  publisher	= academic,
  title		= {Practical Optimization},
  year		= 1981
}

@Article{	  gillsaunshin:1996,
  author	= {P. E. Gill and M. A. Saunders and Joseph R. Shinnerl},
  date-modified	= {2007-09-06 10:22:11 -0700},
  journal	= siammatrix,
  local-url	= {file://localhost/Users/mpf/papers/GillSaunShinn96.pdf},
  month		= {January},
  number	= 1,
  pages		= {35--46},
  title		= {On the stability of {C}holesky factorization for symmetric
		  quasidefinite systems},
  volume	= 17,
  year		= 1996
}

###Article{	  gillsaunshin:1996,
  author	= {P. E. Gill and M. A. Saunders and Joseph R. Shinnerl},
  date-modified	= {2007-09-06 10:22:11 -0700},
  journal	= siammatrix,
  local-url	= {file://localhost/Users/mpf/papers/GillSaunShinn96.pdf},
  month		= {January},
  number	= 1,
  pages		= {35--46},
  title		= {On the stability of {C}holesky factorization for symmetric
		  quasidefinite systems},
  volume	= 17,
  year		= 1996
}

###Article{	  gillsaunshin:1996,
  author	= {P. E. Gill and M. A. Saunders and Joseph R. Shinnerl},
  date-modified	= {2007-09-06 10:22:11 -0700},
  journal	= siammatrix,
  local-url	= {file://localhost/Users/mpf/papers/GillSaunShinn96.pdf},
  month		= {January},
  number	= 1,
  pages		= {35--46},
  title		= {On the stability of {C}holesky factorization for symmetric
		  quasidefinite systems},
  volume	= 17,
  year		= 1996
}

@TechReport{	  gillwong:2010,
  title		= {Sequential quadratic programming methods},
  author	= {Philip E. Gill and Elizabeth Wong},
  type		= {Technical Report},
  number	= {NA-10-03},
  month		= {August},
  year		= 2010,
  institution	= {UCSD Department of Mathematics}
}

###TechReport{	  gillwong:2010,
  title		= {Sequential quadratic programming methods},
  author	= {Philip E. Gill and Elizabeth Wong},
  type		= {Technical Report},
  number	= {NA-10-03},
  month		= {August},
  year		= 2010,
  institution	= {UCSD Department of Mathematics}
}

###TechReport{	  gillwong:2010,
  title		= {Sequential quadratic programming methods},
  author	= {Philip E. Gill and Elizabeth Wong},
  type		= {Technical Report},
  number	= {NA-10-03},
  month		= {August},
  year		= 2010,
  institution	= {UCSD Department of Mathematics}
}

@InProceedings{	  gleich-2011-skew-nuclear,
  author	= {Gleich, David F. and Lim, Lek-Heng},
  title		= {Rank aggregation via nuclear norm minimization},
  booktitle	= {Proceedings of the 17th ACM SIGKDD international
		  conference on Knowledge discovery and data mining},
  year		= 2011,
  series	= {KDD '11},
  pages		= {60--68},
  address	= {New York, NY, USA},
  publisher	= {ACM},
  abstract	= {The process of rank aggregation is intimately intertwined
		  with the structure of skew-symmetric matrices. We apply
		  recent advances in the theory and algorithms of matrix
		  completion to skew-symmetric matrices. This combination of
		  ideas produces a new method for ranking a set of items. The
		  essence of our idea is that a rank aggregation describes a
		  partially filled skew-symmetric matrix. We extend an
		  algorithm for matrix completion to handle skew-symmetric
		  data and use that to extract ranks for each item. Our
		  algorithm applies to both pairwise comparison and rating
		  data. Because it is based on matrix completion, it is
		  robust to both noise and incomplete data. We show a formal
		  recovery result for the noiseless case and present a
		  detailed study of the algorithm on synthetic data and
		  Netflix ratings.},
  acmid		= 2020425,
  doi		= {10.1145/2020408.2020425},
  file		= {:Gleich 2011 - rank aggregation.pdf},
  isbn		= {978-1-4503-0813-7},
  keywords	= {self, nuclear norm, rank aggregation, skew symmetric},
  location	= {San Diego, California, USA},
  numpages	= 9,
  owner		= {David F. Gleich},
  timestamp	= {2010.01.30}
}

###InProceedings{ gleich-2011-skew-nuclear,
  author	= {Gleich, David F. and Lim, Lek-Heng},
  title		= {Rank aggregation via nuclear norm minimization},
  booktitle	= {Proceedings of the 17th ACM SIGKDD international
		  conference on Knowledge discovery and data mining},
  year		= 2011,
  series	= {KDD '11},
  pages		= {60--68},
  address	= {New York, NY, USA},
  publisher	= {ACM},
  abstract	= {The process of rank aggregation is intimately intertwined
		  with the structure of skew-symmetric matrices. We apply
		  recent advances in the theory and algorithms of matrix
		  completion to skew-symmetric matrices. This combination of
		  ideas produces a new method for ranking a set of items. The
		  essence of our idea is that a rank aggregation describes a
		  partially filled skew-symmetric matrix. We extend an
		  algorithm for matrix completion to handle skew-symmetric
		  data and use that to extract ranks for each item. Our
		  algorithm applies to both pairwise comparison and rating
		  data. Because it is based on matrix completion, it is
		  robust to both noise and incomplete data. We show a formal
		  recovery result for the noiseless case and present a
		  detailed study of the algorithm on synthetic data and
		  Netflix ratings.},
  acmid		= 2020425,
  doi		= {10.1145/2020408.2020425},
  file		= {:Gleich 2011 - rank aggregation.pdf},
  isbn		= {978-1-4503-0813-7},
  keywords	= {self, nuclear norm, rank aggregation, skew symmetric},
  location	= {San Diego, California, USA},
  numpages	= 9,
  owner		= {David F. Gleich},
  timestamp	= {2010.01.30}
}

###InProceedings{ gleich-2011-skew-nuclear,
  author	= {Gleich, David F. and Lim, Lek-Heng},
  title		= {Rank aggregation via nuclear norm minimization},
  booktitle	= {Proceedings of the 17th ACM SIGKDD international
		  conference on Knowledge discovery and data mining},
  year		= 2011,
  series	= {KDD '11},
  pages		= {60--68},
  address	= {New York, NY, USA},
  publisher	= {ACM},
  abstract	= {The process of rank aggregation is intimately intertwined
		  with the structure of skew-symmetric matrices. We apply
		  recent advances in the theory and algorithms of matrix
		  completion to skew-symmetric matrices. This combination of
		  ideas produces a new method for ranking a set of items. The
		  essence of our idea is that a rank aggregation describes a
		  partially filled skew-symmetric matrix. We extend an
		  algorithm for matrix completion to handle skew-symmetric
		  data and use that to extract ranks for each item. Our
		  algorithm applies to both pairwise comparison and rating
		  data. Because it is based on matrix completion, it is
		  robust to both noise and incomplete data. We show a formal
		  recovery result for the noiseless case and present a
		  detailed study of the algorithm on synthetic data and
		  Netflix ratings.},
  acmid		= 2020425,
  doi		= {10.1145/2020408.2020425},
  file		= {:Gleich 2011 - rank aggregation.pdf},
  isbn		= {978-1-4503-0813-7},
  keywords	= {self, nuclear norm, rank aggregation, skew symmetric},
  location	= {San Diego, California, USA},
  numpages	= 9,
  owner		= {David F. Gleich},
  timestamp	= {2010.01.30}
}

@Article{	  gloptipoly:2009,
  author	= {D. Henrion and J. B. Lasserre and J. Loefberg},
  title		= {{GloptiPoly} 3: moments, optimization and semidefinite
		  programming},
  journal	= optimmeth,
  volume	= 24,
  number	= {4-5},
  pages		= {761-779},
  year		= 2009,
  url		= {http://homepages.laas.fr/henrion/software/gloptipoly/}
}

###Article{	  gloptipoly:2009,
  author	= {D. Henrion and J. B. Lasserre and J. Loefberg},
  title		= {{GloptiPoly} 3: moments, optimization and semidefinite
		  programming},
  journal	= optimmeth,
  volume	= 24,
  number	= {4-5},
  pages		= {761-779},
  year		= 2009,
  url		= {http://homepages.laas.fr/henrion/software/gloptipoly/}
}

@InProceedings{	  gms03,
  author	= "Gilbert, A. C. and Muthukrishnan, S. and Strauss, M .J.",
  title		= "Approximation of functions over redundant dictionaries
		  using coherence",
  booktitle	= "14th Annual ACM-SIAM Symposium on Discrete Algorithms",
  organization	= "ACM",
  address	= "New York",
  year		= 2003,
  page		= {243--252}
}

###InProceedings{ gms03,
  author	= "Gilbert, A. C. and Muthukrishnan, S. and Strauss, M .J.",
  title		= "Approximation of functions over redundant dictionaries
		  using coherence",
  booktitle	= "14th Annual ACM-SIAM Symposium on Discrete Algorithms",
  organization	= "ACM",
  address	= "New York",
  year		= 2003,
  page		= {243--252}
}

###InProceedings{ gms03,
  author	= "Gilbert, A. C. and Muthukrishnan, S. and Strauss, M .J.",
  title		= "Approximation of functions over redundant dictionaries
		  using coherence",
  booktitle	= "14th Annual ACM-SIAM Symposium on Discrete Algorithms",
  organization	= "ACM",
  address	= "New York",
  year		= 2003,
  page		= {243--252}
}

@Article{	  goemans:1995,
  author	= {Goemans, Michel X. and Williamson, David P.},
  title		= {Improved approximation algorithms for maximum cut and
		  satisfiability problems using semidefinite programming},
  journal	= {J. ACM},
  issue_date	= {Nov. 1995},
  volume	= 42,
  number	= 6,
  month		= nov,
  year		= 1995,
  pages		= {1115--1145},
  numpages	= 31,
  doi		= {10.1145/227683.227684},
  acmid		= 227684,
  publisher	= {ACM},
  address	= {New York, NY, USA},
  keywords	= {Approximation algorithms, convex optimization, randomized
		  algorithms, satisfiability}
}

###Article{	  goemans:1995,
  author	= {Goemans, Michel X. and Williamson, David P.},
  title		= {Improved approximation algorithms for maximum cut and
		  satisfiability problems using semidefinite programming},
  journal	= {J. ACM},
  issue_date	= {Nov. 1995},
  volume	= 42,
  number	= 6,
  month		= nov,
  year		= 1995,
  pages		= {1115--1145},
  numpages	= 31,
  doi		= {10.1145/227683.227684},
  acmid		= 227684,
  publisher	= {ACM},
  address	= {New York, NY, USA},
  keywords	= {Approximation algorithms, convex optimization, randomized
		  algorithms, satisfiability}
}

@Article{	  goffin1999shallow,
  title		= {Shallow, deep and very deep cuts in the analytic center
		  cutting plane method},
  author	= {Goffin, Jean-Louis and Vial, Jean-Philippe},
  journal	= {Mathematical Programming},
  volume	= {84},
  number	= {1},
  pages		= {89--103},
  year		= {1999},
  publisher	= {Springer}
}

@Article{	  goffin1999two,
  title		= {A two-cut approach in the analytic center cutting plane
		  method},
  author	= {Goffin, Jean-Louis and Vial, Jean-Philippe},
  journal	= {Mathematical methods of Operations Research},
  volume	= {49},
  number	= {1},
  pages		= {149--169},
  year		= {1999},
  publisher	= {Springer}
}

@Article{	  goffin98usingthe,
  author	= {Jean-louis Goffin and Faranak Sharifi-Mokhtarian},
  title		= {Using the Primal Dual Infeasible Newton Method in the
		  Analytic Center Method for Problems Defined by Deep Cutting
		  Planes.},
  year		= {1998}
}

@TechReport{	  gok08,
  author	= "Gonzaga, C. C. and Karas, E. W.",
  title		= "Optimal steepest descent algorithms for unconstrained
		  convex problems: fine timing Nesterov's method",
  institution	= "Departnient of Mathematics, Federal University of Santa
		  Catarina",
  address	= "Florian\'opolis",
  year		= 2008,
  month		= "August"
}

###TechReport{	  gok08,
  author	= "Gonzaga, C. C. and Karas, E. W.",
  title		= "Optimal steepest descent algorithms for unconstrained
		  convex problems: fine timing Nesterov's method",
  institution	= "Departnient of Mathematics, Federal University of Santa
		  Catarina",
  address	= "Florian\'opolis",
  year		= 2008,
  month		= "August"
}

###TechReport{	  gok08,
  author	= "Gonzaga, C. C. and Karas, E. W.",
  title		= "Optimal steepest descent algorithms for unconstrained
		  convex problems: fine timing Nesterov's method",
  institution	= "Departnient of Mathematics, Federal University of Santa
		  Catarina",
  address	= "Florian\'opolis",
  year		= 2008,
  month		= "August"
}

@Article{	  golugrei:2003,
  author	= {Gene H. Golub and Chen Greif},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamscicomp,
  number	= 6,
  pages		= {2076--2092},
  title		= {On solving block-structured indefinite linear systems},
  volume	= 24,
  year		= 2003
}

###Article{	  golugrei:2003,
  author	= {Gene H. Golub and Chen Greif},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamscicomp,
  number	= 6,
  pages		= {2076--2092},
  title		= {On solving block-structured indefinite linear systems},
  volume	= 24,
  year		= 2003
}

###Article{	  golugrei:2003,
  author	= {Gene H. Golub and Chen Greif},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamscicomp,
  number	= 6,
  pages		= {2076--2092},
  title		= {On solving block-structured indefinite linear systems},
  volume	= 24,
  year		= 2003
}

@Article{	  golugreivara:2006,
  author	= {G.H. Golub and C. Greif and J.M. Varah},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siammatrix,
  number	= 3,
  pages		= {779-792},
  title		= {An Algebraic Analysis of a Block Diagonal Preconditioner
		  for Saddle Point Systems},
  volume	= 27,
  year		= 2006
}

###Article{	  golugreivara:2006,
  author	= {G.H. Golub and C. Greif and J.M. Varah},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siammatrix,
  number	= 3,
  pages		= {779-792},
  title		= {An Algebraic Analysis of a Block Diagonal Preconditioner
		  for Saddle Point Systems},
  volume	= 27,
  year		= 2006
}

###Article{	  golugreivara:2006,
  author	= {G.H. Golub and C. Greif and J.M. Varah},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siammatrix,
  number	= 3,
  pages		= {779-792},
  title		= {An Algebraic Analysis of a Block Diagonal Preconditioner
		  for Saddle Point Systems},
  volume	= 27,
  year		= 2006
}

@Article{	  goluhansolea:1999,
  author	= {G. H. Golub and P. C Hansen and D. P. O'Leary},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siammatrix,
  number	= 1,
  pages		= {185-194},
  title		= {Tikhonov Regularization and Total Least Squares},
  volume	= 21,
  year		= 1999
}

###Article{	  goluhansolea:1999,
  author	= {G. H. Golub and P. C Hansen and D. P. O'Leary},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siammatrix,
  number	= 1,
  pages		= {185-194},
  title		= {Tikhonov Regularization and Total Least Squares},
  volume	= 21,
  year		= 1999
}

###Article{	  goluhansolea:1999,
  author	= {G. H. Golub and P. C Hansen and D. P. O'Leary},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siammatrix,
  number	= 1,
  pages		= {185-194},
  title		= {Tikhonov Regularization and Total Least Squares},
  volume	= 21,
  year		= 1999
}

@Book{		  goluloan:1989,
  address	= {Baltimore},
  author	= {G. H. Golub and C. F. Van Loan},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {second},
  publisher	= {Johns Hopkins University Press},
  title		= {Matrix Computations},
  year		= 1989
}

###Book{	  goluloan:1989,
  address	= {Baltimore},
  author	= {G. H. Golub and C. F. Van Loan},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {second},
  publisher	= {Johns Hopkins University Press},
  title		= {Matrix Computations},
  year		= 1989
}

###Book{	  goluloan:1989,
  address	= {Baltimore},
  author	= {G. H. Golub and C. F. Van Loan},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {second},
  publisher	= {Johns Hopkins University Press},
  title		= {Matrix Computations},
  year		= 1989
}

@Article{	  golumatt:1991,
  abstract	= {We consider the following problem: Compute a vector $x$
		  such that $\Vert Ax-b\Vert_2 = \min$, subject to the
		  constraint $\Vert x\Vert_2 = \alpha$. A new approach to
		  this problem based on Gauss quadrature is given. The method
		  is especially well suited when the dimensions of $A$ are
		  large and the matrix is sparse. It is also possible to
		  extend this technique to a constrainted quadratic form: For
		  a symmetric matrix $A$ we consider the minimization of
		  $x^TAx - 2b^Tx$ subject to the constraint $\Vert x\Vert_2 =
		  \alpha$. Some numerical examples are given. },
  author	= {Gene H. Golub and Urs von Matt},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Numer. Math.},
  pages		= {561--580},
  title		= {Quadratically constrained least squares and quadratic
		  problems},
  volume	= 59,
  year		= 1991
}

###Article{	  golumatt:1991,
  abstract	= {We consider the following problem: Compute a vector $x$
		  such that $\Vert Ax-b\Vert_2 = \min$, subject to the
		  constraint $\Vert x\Vert_2 = \alpha$. A new approach to
		  this problem based on Gauss quadrature is given. The method
		  is especially well suited when the dimensions of $A$ are
		  large and the matrix is sparse. It is also possible to
		  extend this technique to a constrainted quadratic form: For
		  a symmetric matrix $A$ we consider the minimization of
		  $x^TAx - 2b^Tx$ subject to the constraint $\Vert x\Vert_2 =
		  \alpha$. Some numerical examples are given. },
  author	= {Gene H. Golub and Urs von Matt},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Numer. Math.},
  pages		= {561--580},
  title		= {Quadratically constrained least squares and quadratic
		  problems},
  volume	= 59,
  year		= 1991
}

###Article{	  golumatt:1991,
  abstract	= {We consider the following problem: Compute a vector $x$
		  such that $\Vert Ax-b\Vert_2 = \min$, subject to the
		  constraint $\Vert x\Vert_2 = \alpha$. A new approach to
		  this problem based on Gauss quadrature is given. The method
		  is especially well suited when the dimensions of $A$ are
		  large and the matrix is sparse. It is also possible to
		  extend this technique to a constrainted quadratic form: For
		  a symmetric matrix $A$ we consider the minimization of
		  $x^TAx - 2b^Tx$ subject to the constraint $\Vert x\Vert_2 =
		  \alpha$. Some numerical examples are given. },
  author	= {Gene H. Golub and Urs von Matt},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Numer. Math.},
  pages		= {561--580},
  title		= {Quadratically constrained least squares and quadratic
		  problems},
  volume	= 59,
  year		= 1991
}

@Article{	  goluvonm:1991,
  abstract	= {We consider the following problem: Compute a vector $x$
		  such that $\Vert Ax-b\Vert_2 = \min$, subject to the
		  constraint $\Vert x\Vert_2 = \alpha$. A new approach to
		  this problem based on Gauss quadrature is given. The method
		  is especially well suited when the dimensions of $A$ are
		  large and the matrix is sparse. It is also possible to
		  extend this technique to a constrainted quadratic form: For
		  a symmetric matrix $A$ we consider the minimization of
		  $x^TAx - 2b^Tx$ subject to the constraint $\Vert x\Vert_2 =
		  \alpha$. Some numerical examples are given. },
  author	= {Gene H. Golub and Urs von Matt},
  date-added	= {2007-12-13 11:48:35 -0800},
  date-modified	= {2007-12-13 11:48:35 -0800},
  journal	= {Numer. Math.},
  pages		= {561--580},
  title		= {Quadratically constrained least squares and quadratic
		  problems},
  volume	= 59,
  year		= 1991
}

###Article{	  goluvonm:1991,
  abstract	= {We consider the following problem: Compute a vector $x$
		  such that $\Vert Ax-b\Vert_2 = \min$, subject to the
		  constraint $\Vert x\Vert_2 = \alpha$. A new approach to
		  this problem based on Gauss quadrature is given. The method
		  is especially well suited when the dimensions of $A$ are
		  large and the matrix is sparse. It is also possible to
		  extend this technique to a constrainted quadratic form: For
		  a symmetric matrix $A$ we consider the minimization of
		  $x^TAx - 2b^Tx$ subject to the constraint $\Vert x\Vert_2 =
		  \alpha$. Some numerical examples are given. },
  author	= {Gene H. Golub and Urs von Matt},
  date-added	= {2007-12-13 11:48:35 -0800},
  date-modified	= {2007-12-13 11:48:35 -0800},
  journal	= {Numer. Math.},
  pages		= {561--580},
  title		= {Quadratically constrained least squares and quadratic
		  problems},
  volume	= 59,
  year		= 1991
}

###Article{	  goluvonm:1991,
  abstract	= {We consider the following problem: Compute a vector $x$
		  such that $\Vert Ax-b\Vert_2 = \min$, subject to the
		  constraint $\Vert x\Vert_2 = \alpha$. A new approach to
		  this problem based on Gauss quadrature is given. The method
		  is especially well suited when the dimensions of $A$ are
		  large and the matrix is sparse. It is also possible to
		  extend this technique to a constrainted quadratic form: For
		  a symmetric matrix $A$ we consider the minimization of
		  $x^TAx - 2b^Tx$ subject to the constraint $\Vert x\Vert_2 =
		  \alpha$. Some numerical examples are given. },
  author	= {Gene H. Golub and Urs von Matt},
  date-added	= {2007-12-13 11:48:35 -0800},
  date-modified	= {2007-12-13 11:48:35 -0800},
  journal	= {Numer. Math.},
  pages		= {561--580},
  title		= {Quadratically constrained least squares and quadratic
		  problems},
  volume	= 59,
  year		= 1991
}

@TechReport{	  gondgrot:2006,
  author	= {J. Gondzio and A. Grothey},
  date-added	= {2007-10-12 18:19:15 -0700},
  date-modified	= {2007-10-12 18:20:31 -0700},
  institution	= {School of Mathematics, University of Edinburgh},
  number	= {MS-06-005},
  title		= {A new unblocking technique to warmstart interior-point
		  methods based on sensitivity analysis},
  type		= {Tech. rep.},
  year		= 2006
}

###TechReport{	  gondgrot:2006,
  author	= {J. Gondzio and A. Grothey},
  date-added	= {2007-10-12 18:19:15 -0700},
  date-modified	= {2007-10-12 18:20:31 -0700},
  institution	= {School of Mathematics, University of Edinburgh},
  number	= {MS-06-005},
  title		= {A new unblocking technique to warmstart interior-point
		  methods based on sensitivity analysis},
  type		= {Tech. rep.},
  year		= 2006
}

###TechReport{	  gondgrot:2006,
  author	= {J. Gondzio and A. Grothey},
  date-added	= {2007-10-12 18:19:15 -0700},
  date-modified	= {2007-10-12 18:20:31 -0700},
  institution	= {School of Mathematics, University of Edinburgh},
  number	= {MS-06-005},
  title		= {A new unblocking technique to warmstart interior-point
		  methods based on sensitivity analysis},
  type		= {Tech. rep.},
  year		= 2006
}

@TechReport{	  gonz:2003,
  address	= {Santa Catarina, Brazil},
  author	= {C. C. Gonzaga},
  date-modified	= {2007-07-26 16:41:38 -0700},
  institution	= {Dept.~of Mathematics, Federal University of Santa
		  Catarina},
  month		= {April},
  title		= {Generation of degenerate linear programming problems},
  year		= 2003
}

###TechReport{	  gonz:2003,
  address	= {Santa Catarina, Brazil},
  author	= {C. C. Gonzaga},
  date-modified	= {2007-07-26 16:41:38 -0700},
  institution	= {Dept.~of Mathematics, Federal University of Santa
		  Catarina},
  month		= {April},
  title		= {Generation of degenerate linear programming problems},
  year		= 2003
}

###TechReport{	  gonz:2003,
  address	= {Santa Catarina, Brazil},
  author	= {C. C. Gonzaga},
  date-modified	= {2007-07-26 16:41:38 -0700},
  institution	= {Dept.~of Mathematics, Federal University of Santa
		  Catarina},
  month		= {April},
  title		= {Generation of degenerate linear programming problems},
  year		= 2003
}

@Article{	  gotsgushef:2003,
  author	= {C. Gotsman and X. Gu and A. Sheffer},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {ACM Transactions on Graphics},
  number	= 3,
  pages		= {358--363},
  title		= {Fundamentals of Spherical Parameterization for {3D}
		  Meshes},
  volume	= 22,
  year		= 2003
}

###Article{	  gotsgushef:2003,
  author	= {C. Gotsman and X. Gu and A. Sheffer},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {ACM Transactions on Graphics},
  number	= 3,
  pages		= {358--363},
  title		= {Fundamentals of Spherical Parameterization for {3D}
		  Meshes},
  volume	= 22,
  year		= 2003
}

###Article{	  gotsgushef:2003,
  author	= {C. Gotsman and X. Gu and A. Sheffer},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {ACM Transactions on Graphics},
  number	= 3,
  pages		= {358--363},
  title		= {Fundamentals of Spherical Parameterization for {3D}
		  Meshes},
  volume	= 22,
  year		= 2003
}

@Article{	  goul:1989,
  author	= {N. I. M. Gould},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamnumanal,
  pages		= {107--128},
  title		= {On the convergence of a sequential penalty function method
		  for constrained minimization},
  volume	= 26,
  year		= 1989
}

###Article{	  goul:1989,
  author	= {N. I. M. Gould},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamnumanal,
  pages		= {107--128},
  title		= {On the convergence of a sequential penalty function method
		  for constrained minimization},
  volume	= 26,
  year		= 1989
}

###Article{	  goul:1989,
  author	= {N. I. M. Gould},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamnumanal,
  pages		= {107--128},
  title		= {On the convergence of a sequential penalty function method
		  for constrained minimization},
  volume	= 26,
  year		= 1989
}

@Article{	  gouldtoint:2010,
  author	= {N. I. M. Gould and Ph. L. Toint},
  title		= {Nonlinear programming without a penalty function or a
		  filter},
  journal	= mathprog,
  volume	= 122,
  number	= 1,
  year		= 2010,
  page		= {155--196},
  note		= {Erratum: Math.\@ Prog.\@ B, 131(1-2) (2011) pp.403-404}
}

@Article{	  gouleytoi:2004,
  author	= {N. I. M. Gould and S. Leyffer and \mbox{Ph.} L. Toint},
  title		= {A multidimensional filter algorithm for nonlinear
		  equations and nonlinear least-squares},
  journal	= siamopt,
  year		= 2004,
  volume	= 15,
  number	= 1,
  pages		= {17--38}
}

###Article{	  gouleytoi:2004,
  author	= {N. I. M. Gould and S. Leyffer and \mbox{Ph.} L. Toint},
  title		= {A multidimensional filter algorithm for nonlinear
		  equations and nonlinear least-squares},
  journal	= siamopt,
  year		= 2004,
  volume	= 15,
  number	= 1,
  pages		= {17--38}
}

###Article{	  gouleytoi:2004,
  author	= {N. I. M. Gould and S. Leyffer and \mbox{Ph.} L. Toint},
  title		= {A multidimensional filter algorithm for nonlinear
		  equations and nonlinear least-squares},
  journal	= siamopt,
  year		= 2004,
  volume	= 15,
  number	= 1,
  pages		= {17--38}
}

@Article{	  goulhribnoce:2001,
  author	= {Nicholas I. M. Gould and Mary E. Hribar and Jorge
		  Nocedal},
  doi		= {10.1137/S1064827598345667},
  journal	= siamcomp,
  keywords	= {nonlinear optimization; conjugate gradient method;
		  quadratic programming; preconditioning; iterative
		  refinement},
  number	= 4,
  pages		= {1376-1395},
  publisher	= {SIAM},
  title		= {On the Solution of Equality Constrained Quadratic
		  Programming Problems Arising in Optimization},
  url		= {http://link.aip.org/link/?SCE/23/1376/1},
  volume	= 23,
  year		= 2001
}

###Article{	  goulhribnoce:2001,
  author	= {Nicholas I. M. Gould and Mary E. Hribar and Jorge
		  Nocedal},
  doi		= {10.1137/S1064827598345667},
  journal	= siamcomp,
  keywords	= {nonlinear optimization; conjugate gradient method;
		  quadratic programming; preconditioning; iterative
		  refinement},
  number	= 4,
  pages		= {1376-1395},
  publisher	= {SIAM},
  title		= {On the Solution of Equality Constrained Quadratic
		  Programming Problems Arising in Optimization},
  url		= {http://link.aip.org/link/?SCE/23/1376/1},
  volume	= 23,
  year		= 2001
}

###Article{	  goulhribnoce:2001,
  author	= {Nicholas I. M. Gould and Mary E. Hribar and Jorge
		  Nocedal},
  doi		= {10.1137/S1064827598345667},
  journal	= siamcomp,
  keywords	= {nonlinear optimization; conjugate gradient method;
		  quadratic programming; preconditioning; iterative
		  refinement},
  number	= 4,
  pages		= {1376-1395},
  publisher	= {SIAM},
  title		= {On the Solution of Equality Constrained Quadratic
		  Programming Problems Arising in Optimization},
  url		= {http://link.aip.org/link/?SCE/23/1376/1},
  volume	= 23,
  year		= 2001
}

@Article{	  goulluciromatoin:1999,
  author	= {Nicholas I. M. Gould and Stefano Lucidi and Massimo Roma
		  and Philippe L. Toint},
  doi		= {10.1137/S1052623497322735},
  journal	= siamopt,
  keywords	= {trust-region subproblem; Lanczos method; conjugate
		  gradients; preconditioning},
  local-url	= {file://localhost/Users/mpf/papers/GoulLuciToin99.pdf},
  number	= 2,
  pages		= {504-525},
  publisher	= {SIAM},
  title		= {Solving the Trust-Region Subproblem using the Lanczos
		  Method},
  url		= {http://link.aip.org/link/?SJE/9/504/1},
  volume	= 9,
  year		= 1999
}

###Article{	  goulluciromatoin:1999,
  author	= {Nicholas I. M. Gould and Stefano Lucidi and Massimo Roma
		  and Philippe L. Toint},
  doi		= {10.1137/S1052623497322735},
  journal	= siamopt,
  keywords	= {trust-region subproblem; Lanczos method; conjugate
		  gradients; preconditioning},
  local-url	= {file://localhost/Users/mpf/papers/GoulLuciToin99.pdf},
  number	= 2,
  pages		= {504-525},
  publisher	= {SIAM},
  title		= {Solving the Trust-Region Subproblem using the Lanczos
		  Method},
  url		= {http://link.aip.org/link/?SJE/9/504/1},
  volume	= 9,
  year		= 1999
}

###Article{	  goulluciromatoin:1999,
  author	= {Nicholas I. M. Gould and Stefano Lucidi and Massimo Roma
		  and Philippe L. Toint},
  doi		= {10.1137/S1052623497322735},
  journal	= siamopt,
  keywords	= {trust-region subproblem; Lanczos method; conjugate
		  gradients; preconditioning},
  local-url	= {file://localhost/Users/mpf/papers/GoulLuciToin99.pdf},
  number	= 2,
  pages		= {504-525},
  publisher	= {SIAM},
  title		= {Solving the Trust-Region Subproblem using the Lanczos
		  Method},
  url		= {http://link.aip.org/link/?SJE/9/504/1},
  volume	= 9,
  year		= 1999
}

@Article{	  goulorbatoin:2003,
  author	= {N. I. M. Gould and D. Orban and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-09-14 15:58:13 -0700},
  journal	= acmmathsoft,
  local-url	= {file://localhost/Users/mpf/papers/CUTEr.ps},
  month		= dec,
  number	= 4,
  pages		= {373--394},
  title		= {{CUTEr} and {SifDec}: A Constrained and Unconstrained
		  Testing Environment, revisited},
  url		= {http://doi.acm.org/10.1145/962437.962439},
  volume	= 29,
  year		= 2003
}

###Article{	  goulorbatoin:2003,
  author	= {N. I. M. Gould and D. Orban and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-09-14 15:58:13 -0700},
  journal	= acmmathsoft,
  local-url	= {file://localhost/Users/mpf/papers/CUTEr.ps},
  month		= dec,
  number	= 4,
  pages		= {373--394},
  title		= {{CUTEr} and {SifDec}: A Constrained and Unconstrained
		  Testing Environment, revisited},
  url		= {http://doi.acm.org/10.1145/962437.962439},
  volume	= 29,
  year		= 2003
}

###Article{	  goulorbatoin:2003,
  author	= {N. I. M. Gould and D. Orban and {\mbox{Ph}}. L. Toint},
  date-modified	= {2007-09-14 15:58:13 -0700},
  journal	= acmmathsoft,
  local-url	= {file://localhost/Users/mpf/papers/CUTEr.ps},
  month		= dec,
  number	= 4,
  pages		= {373--394},
  title		= {{CUTEr} and {SifDec}: A Constrained and Unconstrained
		  Testing Environment, revisited},
  url		= {http://doi.acm.org/10.1145/962437.962439},
  volume	= 29,
  year		= 2003
}

@Article{	  goulorbatoin:2004,
  author	= {N.I.M. Gould and D. Orban and {Ph}.L. Toint},
  title		= {{GALAHAD}, a library of thread-safe {Fortran} 90 packages
		  for large-scale nonlinear optimization},
  journal	= acmmathsoft,
  volume	= 29,
  number	= 4,
  pages		= {353-372},
  year		= 2004
}

###Article{	  goulorbatoin:2004,
  author	= {N.I.M. Gould and D. Orban and {Ph}.L. Toint},
  title		= {{GALAHAD}, a library of thread-safe {Fortran} 90 packages
		  for large-scale nonlinear optimization},
  journal	= acmmathsoft,
  volume	= 29,
  number	= 4,
  pages		= {353-372},
  year		= 2004
}

###Article{	  goulorbatoin:2004,
  author	= {N.I.M. Gould and D. Orban and {Ph}.L. Toint},
  title		= {{GALAHAD}, a library of thread-safe {Fortran} 90 packages
		  for large-scale nonlinear optimization},
  journal	= acmmathsoft,
  volume	= 29,
  number	= 4,
  pages		= {353-372},
  year		= 2004
}

@InCollection{	  goultoin:2000,
  author	= {N. I. M. Gould and \mbox{Ph}. L. Toint},
  title		= {{SQP} methods for large-scale nonlinear programming},
  booktitle	= {System Modelling and Optimization, Methods, Theory and
		  Applications},
  pages		= {149-178},
  publisher	= {Kluwer Academic},
  address	= {Boston},
  year		= 2000,
  editor	= {M. J. D. Powell and S. Scholtes}
}

###InCollection{  goultoin:2000,
  author	= {N. I. M. Gould and \mbox{Ph}. L. Toint},
  title		= {{SQP} methods for large-scale nonlinear programming},
  booktitle	= {System Modelling and Optimization, Methods, Theory and
		  Applications},
  pages		= {149-178},
  publisher	= {Kluwer Academic},
  address	= {Boston},
  year		= 2000,
  editor	= {M. J. D. Powell and S. Scholtes}
}

###InCollection{  goultoin:2000,
  author	= {N. I. M. Gould and \mbox{Ph}. L. Toint},
  title		= {{SQP} methods for large-scale nonlinear programming},
  booktitle	= {System Modelling and Optimization, Methods, Theory and
		  Applications},
  pages		= {149-178},
  publisher	= {Kluwer Academic},
  address	= {Boston},
  year		= 2000,
  editor	= {M. J. D. Powell and S. Scholtes}
}

@InCollection{	  goultoin:2002a,
  author	= {N. I. M. Gould and \mbox{Ph}. L. Toint},
  title		= {Numerical methods for large-scale non-convex quadratic
		  programming},
  booktitle	= {Trends in Industrial and Applied Mathematics},
  pages		= {149-179},
  publisher	= {Kluwer Academic},
  address	= {Deordrecht, The Netherlands},
  year		= 2002,
  editor	= {A. H. Siddiqi and M. Kocvara}
}

###InCollection{  goultoin:2002a,
  author	= {N. I. M. Gould and \mbox{Ph}. L. Toint},
  title		= {Numerical methods for large-scale non-convex quadratic
		  programming},
  booktitle	= {Trends in Industrial and Applied Mathematics},
  pages		= {149-179},
  publisher	= {Kluwer Academic},
  address	= {Deordrecht, The Netherlands},
  year		= 2002,
  editor	= {A. H. Siddiqi and M. Kocvara}
}

###InCollection{  goultoin:2002a,
  author	= {N. I. M. Gould and \mbox{Ph}. L. Toint},
  title		= {Numerical methods for large-scale non-convex quadratic
		  programming},
  booktitle	= {Trends in Industrial and Applied Mathematics},
  pages		= {149-179},
  publisher	= {Kluwer Academic},
  address	= {Deordrecht, The Netherlands},
  year		= 2002,
  editor	= {A. H. Siddiqi and M. Kocvara}
}

@Article{	  goultoin:2002b,
  abstract	= {We consider a working-set method for solving large-scale
		  quadratic programming problems for which there is no
		  requirement that the objective function be convex. The
		  methods are iterative at two levels, one level relating to
		  the selection of the current working set, and the second
		  due to the method used to solve the equality-constrained
		  problem for this working set. A preconditioned conjugate
		  gradient method is used for this inner iteration, with the
		  preconditioner chosen especially to ensure feasibility of
		  the iterates. The preconditioner is updated at the
		  conclusion of each outer iteration to ensure that this
		  feasibility requirement persists. The well-known
		  equivalence between the conjugate-gradient and Lanczos
		  methods is exploited when finding directions of negative
		  curvature. Details of an implementation---the Fortran 90
		  package {\tt QPA} in the forthcoming {\sf GALAHAD}
		  library---are given.},
  author	= {Nicholas I. M. Gould and {\mbox{Ph}}. L. Toint},
  title		= {An iterative working-set method for large-scale nonconvex
		  quadratic programming},
  journal	= appnummath,
  year		= 2002,
  volume	= 43,
  pages		= {109-128}
}

###Article{	  goultoin:2002b,
  abstract	= {We consider a working-set method for solving large-scale
		  quadratic programming problems for which there is no
		  requirement that the objective function be convex. The
		  methods are iterative at two levels, one level relating to
		  the selection of the current working set, and the second
		  due to the method used to solve the equality-constrained
		  problem for this working set. A preconditioned conjugate
		  gradient method is used for this inner iteration, with the
		  preconditioner chosen especially to ensure feasibility of
		  the iterates. The preconditioner is updated at the
		  conclusion of each outer iteration to ensure that this
		  feasibility requirement persists. The well-known
		  equivalence between the conjugate-gradient and Lanczos
		  methods is exploited when finding directions of negative
		  curvature. Details of an implementation---the Fortran 90
		  package {\tt QPA} in the forthcoming {\sf GALAHAD}
		  library---are given.},
  author	= {Nicholas I. M. Gould and {\mbox{Ph}}. L. Toint},
  title		= {An iterative working-set method for large-scale nonconvex
		  quadratic programming},
  journal	= appnummath,
  year		= 2002,
  volume	= 43,
  pages		= {109-128}
}

###Article{	  goultoin:2002b,
  abstract	= {We consider a working-set method for solving large-scale
		  quadratic programming problems for which there is no
		  requirement that the objective function be convex. The
		  methods are iterative at two levels, one level relating to
		  the selection of the current working set, and the second
		  due to the method used to solve the equality-constrained
		  problem for this working set. A preconditioned conjugate
		  gradient method is used for this inner iteration, with the
		  preconditioner chosen especially to ensure feasibility of
		  the iterates. The preconditioner is updated at the
		  conclusion of each outer iteration to ensure that this
		  feasibility requirement persists. The well-known
		  equivalence between the conjugate-gradient and Lanczos
		  methods is exploited when finding directions of negative
		  curvature. Details of an implementation---the Fortran 90
		  package {\tt QPA} in the forthcoming {\sf GALAHAD}
		  library---are given.},
  author	= {Nicholas I. M. Gould and {\mbox{Ph}}. L. Toint},
  title		= {An iterative working-set method for large-scale nonconvex
		  quadratic programming},
  journal	= appnummath,
  year		= 2002,
  volume	= 43,
  pages		= {109-128}
}

@Article{	  goy05,
  author	= "Goldfarb, D. and Yin, W.",
  title		= "Second-order cone programming methods for total
		  variation-based image restoration",
  journal	= siamcomp,
  year		= 2005,
  volume	= 27,
  page		= {622--645}
}

###Article{	  goy05,
  author	= "Goldfarb, D. and Yin, W.",
  title		= "Second-order cone programming methods for total
		  variation-based image restoration",
  journal	= siamcomp,
  year		= 2005,
  volume	= 27,
  page		= {622--645}
}

###Article{	  goy05,
  author	= "Goldfarb, D. and Yin, W.",
  title		= "Second-order cone programming methods for total
		  variation-based image restoration",
  journal	= siamcomp,
  year		= 2005,
  volume	= 27,
  page		= {622--645}
}

@InCollection{	  granboyd:2008,
  author	= {M. Grant and S. Boyd},
  editor	= {V. Blondel, S. Boyd, and H. Kimura},
  title		= {Lecture Notes in Control and Information Sciences},
  chapter	= {Graph implementations for nonsmooth convex programs},
  publisher	= {Springer},
  year		= 2008,
  pages		= {95--110}
}

###InCollection{  granboyd:2008,
  author	= {M. Grant and S. Boyd},
  editor	= {V. Blondel, S. Boyd, and H. Kimura},
  title		= {Lecture Notes in Control and Information Sciences},
  chapter	= {Graph implementations for nonsmooth convex programs},
  publisher	= {Springer},
  year		= 2008,
  pages		= {95--110}
}

###InCollection{  granboyd:2008,
  author	= {M. Grant and S. Boyd},
  editor	= {V. Blondel, S. Boyd, and H. Kimura},
  title		= {Lecture Notes in Control and Information Sciences},
  chapter	= {Graph implementations for nonsmooth convex programs},
  publisher	= {Springer},
  year		= 2008,
  pages		= {95--110}
}

@Article{	  greirees:2007,
  abstract	= {We explore a preconditioning technique applied to the
		  problem of solving linear systems arising from primal-dual
		  interior point algorithms in linear and quadratic
		  programming. The preconditioner has the attractive property
		  of improved eigenvalue clustering with increased
		  illconditioning of the (1,1) block of the saddle point
		  matrix. It fits well into the optimization framework since
		  the interior point iterates yield increasingly
		  ill-conditioned linear systems as the solution is
		  approached. We analyze the spectral characteristics of the
		  preconditioner, utilizing projections onto the null space
		  of the constraint matrix, and demonstrate performance on
		  problems from the NETLIB and CUTEr test suites. The
		  numerical experiments include results based on inexact
		  inner iterations.},
  author	= {C. Greif and T. Rees},
  journal	= siamcomp,
  number	= 5,
  pages		= {1992-2007},
  title		= {A Preconditioner for Linear Systems Arising from Interior
		  Point Optimization Methods},
  url		= {http://www.cs.ubc.ca/~greif/Papers/rg2006.pdf},
  volume	= 29,
  year		= 2007
}

###Article{	  greirees:2007,
  abstract	= {We explore a preconditioning technique applied to the
		  problem of solving linear systems arising from primal-dual
		  interior point algorithms in linear and quadratic
		  programming. The preconditioner has the attractive property
		  of improved eigenvalue clustering with increased
		  illconditioning of the (1,1) block of the saddle point
		  matrix. It fits well into the optimization framework since
		  the interior point iterates yield increasingly
		  ill-conditioned linear systems as the solution is
		  approached. We analyze the spectral characteristics of the
		  preconditioner, utilizing projections onto the null space
		  of the constraint matrix, and demonstrate performance on
		  problems from the NETLIB and CUTEr test suites. The
		  numerical experiments include results based on inexact
		  inner iterations.},
  author	= {C. Greif and T. Rees},
  journal	= siamcomp,
  number	= 5,
  pages		= {1992-2007},
  title		= {A Preconditioner for Linear Systems Arising from Interior
		  Point Optimization Methods},
  url		= {http://www.cs.ubc.ca/~greif/Papers/rg2006.pdf},
  volume	= 29,
  year		= 2007
}

###Article{	  greirees:2007,
  abstract	= {We explore a preconditioning technique applied to the
		  problem of solving linear systems arising from primal-dual
		  interior point algorithms in linear and quadratic
		  programming. The preconditioner has the attractive property
		  of improved eigenvalue clustering with increased
		  illconditioning of the (1,1) block of the saddle point
		  matrix. It fits well into the optimization framework since
		  the interior point iterates yield increasingly
		  ill-conditioned linear systems as the solution is
		  approached. We analyze the spectral characteristics of the
		  preconditioner, utilizing projections onto the null space
		  of the constraint matrix, and demonstrate performance on
		  problems from the NETLIB and CUTEr test suites. The
		  numerical experiments include results based on inexact
		  inner iterations.},
  author	= {C. Greif and T. Rees},
  journal	= siamcomp,
  number	= 5,
  pages		= {1992-2007},
  title		= {A Preconditioner for Linear Systems Arising from Interior
		  Point Optimization Methods},
  url		= {http://www.cs.ubc.ca/~greif/Papers/rg2006.pdf},
  volume	= 29,
  year		= 2007
}

@Article{	  gribonval2003,
  author	= "Gribonval, R. and Nielsen. M.",
  title		= "Sparse representations in unions of bases",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2003,
  volume	= 49,
  page		= {3320--3325}
}

###Article{	  gribonval2003,
  author	= "Gribonval, R. and Nielsen. M.",
  title		= "Sparse representations in unions of bases",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2003,
  volume	= 49,
  page		= {3320--3325}
}

###Article{	  gribonval2003,
  author	= "Gribonval, R. and Nielsen. M.",
  title		= "Sparse representations in unions of bases",
  journal	= "Information Theory, IEEE Transactions",
  year		= 2003,
  volume	= 49,
  page		= {3320--3325}
}

@Article{	  grifstew:1961,
  author	= {R. E. Griffith and R. A. Stewart},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= managesci,
  pages		= {379-392},
  title		= {A Nonlinear Programming Technique for the Optimization of
		  Continuous Processing Systems},
  volume	= 7,
  year		= 1961
}

###Article{	  grifstew:1961,
  author	= {R. E. Griffith and R. A. Stewart},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= managesci,
  pages		= {379-392},
  title		= {A Nonlinear Programming Technique for the Optimization of
		  Continuous Processing Systems},
  volume	= 7,
  year		= 1961
}

###Article{	  grifstew:1961,
  author	= {R. E. Griffith and R. A. Stewart},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= managesci,
  pages		= {379-392},
  title		= {A Nonlinear Programming Technique for the Optimization of
		  Continuous Processing Systems},
  volume	= 7,
  year		= 1961
}

@Article{	  grippo1994class,
  title		= {A class of unconstrained minimization methods for neural
		  network training},
  author	= {Grippo, L.},
  journal	= optimmeth,
  volume	= 4,
  number	= 2,
  pages		= {135--150},
  year		= 1994,
  publisher	= {Taylor \& Francis}
}

###Article{	  grippo1994class,
  title		= {A class of unconstrained minimization methods for neural
		  network training},
  author	= {Grippo, L.},
  journal	= optimmeth,
  volume	= 4,
  number	= 2,
  pages		= {135--150},
  year		= 1994,
  publisher	= {Taylor \& Francis}
}

###Article{	  grippo1994class,
  title		= {A class of unconstrained minimization methods for neural
		  network training},
  author	= {Grippo, L.},
  journal	= optimmeth,
  volume	= 4,
  number	= 2,
  pages		= {135--150},
  year		= 1994,
  publisher	= {Taylor \& Francis}
}

@Article{	  gripscia:1999,
  author	= {L. Grippo and M. Sciandrone},
  title		= {On the Convergence of the Block Nonlinear Gauss-Seidel
		  Method under Convex Constraints},
  journal	= {Operations Research Letter 26},
  year		= 1999,
  pages		= {127-136}
}

###Article{	  gripscia:1999,
  author	= {L. Grippo and M. Sciandrone},
  title		= {On the Convergence of the Block Nonlinear Gauss-Seidel
		  Method under Convex Constraints},
  journal	= {Operations Research Letter 26},
  year		= 1999,
  pages		= {127-136}
}

###Article{	  gripscia:1999,
  author	= {L. Grippo and M. Sciandrone},
  title		= {On the Convergence of the Block Nonlinear Gauss-Seidel
		  Method under Convex Constraints},
  journal	= {Operations Research Letter 26},
  year		= 1999,
  pages		= {127-136}
}

@Article{	  grn03,
  abstract	= {The purpose of this correspondence is to generalize a
		  result by Donoho and Huo and Elad and Bruckstein on sparse
		  representations of signals in a union of two orthonormal
		  bases for $\mathbb{R}^N$. We consider general (redundant)
		  dictionaries for $\mathbb{R}^N$, and derive sufficient
		  conditions for having unique sparse representations of
		  signals in such dictionaries. The special case where the
		  dictionary is given by the union of $L \geq 2$ orthonormal
		  bases for $\mathbb{R}^N$ is studied in more detail. In
		  particular, it is proved that the result of Donoho and Huo,
		  concerning the replacement of the $\ell^0$ optimization
		  problem with a linear programming problem when searching
		  for sparse representations, has an analog for dictionaries
		  that may be highly redundant.},
  author	= {R{\'e}mi Gribonval and Morten Nielsen},
  journal	= ieeetransinfo,
  keywords	= {Dictionaries, Grassmannian frames, linear programming,
		  mutually incoherent bases, nonlinear approximation, sparse
		  representations},
  month		= {December},
  number	= 12,
  pages		= {3320--3325},
  title		= {Sparse Representations in Unions of Bases},
  url		= {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1255564&isnumber=28084},
  volume	= 49,
  year		= 2003
}

###Article{	  grn03,
  abstract	= {The purpose of this correspondence is to generalize a
		  result by Donoho and Huo and Elad and Bruckstein on sparse
		  representations of signals in a union of two orthonormal
		  bases for $\mathbb{R}^N$. We consider general (redundant)
		  dictionaries for $\mathbb{R}^N$, and derive sufficient
		  conditions for having unique sparse representations of
		  signals in such dictionaries. The special case where the
		  dictionary is given by the union of $L \geq 2$ orthonormal
		  bases for $\mathbb{R}^N$ is studied in more detail. In
		  particular, it is proved that the result of Donoho and Huo,
		  concerning the replacement of the $\ell^0$ optimization
		  problem with a linear programming problem when searching
		  for sparse representations, has an analog for dictionaries
		  that may be highly redundant.},
  author	= {R{\'e}mi Gribonval and Morten Nielsen},
  journal	= ieeetransinfo,
  keywords	= {Dictionaries, Grassmannian frames, linear programming,
		  mutually incoherent bases, nonlinear approximation, sparse
		  representations},
  month		= {December},
  number	= 12,
  pages		= {3320--3325},
  title		= {Sparse Representations in Unions of Bases},
  url		= {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1255564&isnumber=28084},
  volume	= 49,
  year		= 2003
}

###Article{	  grn03,
  abstract	= {The purpose of this correspondence is to generalize a
		  result by Donoho and Huo and Elad and Bruckstein on sparse
		  representations of signals in a union of two orthonormal
		  bases for $\mathbb{R}^N$. We consider general (redundant)
		  dictionaries for $\mathbb{R}^N$, and derive sufficient
		  conditions for having unique sparse representations of
		  signals in such dictionaries. The special case where the
		  dictionary is given by the union of $L \geq 2$ orthonormal
		  bases for $\mathbb{R}^N$ is studied in more detail. In
		  particular, it is proved that the result of Donoho and Huo,
		  concerning the replacement of the $\ell^0$ optimization
		  problem with a linear programming problem when searching
		  for sparse representations, has an analog for dictionaries
		  that may be highly redundant.},
  author	= {R{\'e}mi Gribonval and Morten Nielsen},
  journal	= ieeetransinfo,
  keywords	= {Dictionaries, Grassmannian frames, linear programming,
		  mutually incoherent bases, nonlinear approximation, sparse
		  representations},
  month		= {December},
  number	= 12,
  pages		= {3320--3325},
  title		= {Sparse Representations in Unions of Bases},
  url		= {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1255564&isnumber=28084},
  volume	= 49,
  year		= 2003
}

@TechReport{	  grodwolk:2005,
  abstract	= {We present a new method for regularization of
		  ill-conditioned problems, such as those that arise in image
		  restoration or mathematical processing of medical data. The
		  method extends the traditional trust-region subproblem,
		  TRS, approach that makes use of the L-curve maximum
		  curvature criterion, a strategy recently proposed to find a
		  good regularization parameter. We use derivative
		  information, and properties of an algorithm for solving the
		  TRS, to efficiently move along points on the L-curve and
		  reach the point of maximum curvature. We do not find a
		  complete characterization of the L-curve. A MATLAB code for
		  the algorithm is tested and a comparison to the conjugate
		  gradient least squares, CGLS, approach is given and
		  analyzed. },
  address	= {Waterloo, Canada},
  author	= {Oleg Grodzevich and Henry Wolkowicz},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Combinatorics \& Optimization, University of
		  Waterloo},
  keywords	= {Regularization, trust region subproblem, ill-conditioned
		  problems, L-curve, image restoration},
  month		= {May},
  number	= {Research Report CORR 2005-11},
  title		= {Regularization using a parameterized trust region
		  subproblem},
  year		= 2005
}

###TechReport{	  grodwolk:2005,
  abstract	= {We present a new method for regularization of
		  ill-conditioned problems, such as those that arise in image
		  restoration or mathematical processing of medical data. The
		  method extends the traditional trust-region subproblem,
		  TRS, approach that makes use of the L-curve maximum
		  curvature criterion, a strategy recently proposed to find a
		  good regularization parameter. We use derivative
		  information, and properties of an algorithm for solving the
		  TRS, to efficiently move along points on the L-curve and
		  reach the point of maximum curvature. We do not find a
		  complete characterization of the L-curve. A MATLAB code for
		  the algorithm is tested and a comparison to the conjugate
		  gradient least squares, CGLS, approach is given and
		  analyzed. },
  address	= {Waterloo, Canada},
  author	= {Oleg Grodzevich and Henry Wolkowicz},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Combinatorics \& Optimization, University of
		  Waterloo},
  keywords	= {Regularization, trust region subproblem, ill-conditioned
		  problems, L-curve, image restoration},
  month		= {May},
  number	= {Research Report CORR 2005-11},
  title		= {Regularization using a parameterized trust region
		  subproblem},
  year		= 2005
}

###TechReport{	  grodwolk:2005,
  abstract	= {We present a new method for regularization of
		  ill-conditioned problems, such as those that arise in image
		  restoration or mathematical processing of medical data. The
		  method extends the traditional trust-region subproblem,
		  TRS, approach that makes use of the L-curve maximum
		  curvature criterion, a strategy recently proposed to find a
		  good regularization parameter. We use derivative
		  information, and properties of an algorithm for solving the
		  TRS, to efficiently move along points on the L-curve and
		  reach the point of maximum curvature. We do not find a
		  complete characterization of the L-curve. A MATLAB code for
		  the algorithm is tested and a comparison to the conjugate
		  gradient least squares, CGLS, approach is given and
		  analyzed. },
  address	= {Waterloo, Canada},
  author	= {Oleg Grodzevich and Henry Wolkowicz},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Combinatorics \& Optimization, University of
		  Waterloo},
  keywords	= {Regularization, trust region subproblem, ill-conditioned
		  problems, L-curve, image restoration},
  month		= {May},
  number	= {Research Report CORR 2005-11},
  title		= {Regularization using a parameterized trust region
		  subproblem},
  year		= 2005
}

@Book{		  grotschel2012geometric,
  title		= {Geometric algorithms and combinatorial optimization},
  author	= {Gr{\"o}tschel, Martin and Lov{\'a}sz, L{\'a}szl{\'o} and
		  Schrijver, Alexander},
  volume	= {2},
  year		= {2012},
  publisher	= {Springer Science \& Business Media}
}

@Article{	  guddwackzule:1984,
  author	= {J\"{u}rgen Guddat and Hansj\"{o}rg Wacker and W. Zulehner},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {79-96},
  title		= {On Imbedding and Parametric Optimization---A Concept of a
		  Globally Convergent Algorithm for Nonlinear Optimization
		  Problems},
  volume	= 21,
  year		= 1984
}

###Article{	  guddwackzule:1984,
  author	= {J\"{u}rgen Guddat and Hansj\"{o}rg Wacker and W. Zulehner},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {79-96},
  title		= {On Imbedding and Parametric Optimization---A Concept of a
		  Globally Convergent Algorithm for Nonlinear Optimization
		  Problems},
  volume	= 21,
  year		= 1984
}

###Article{	  guddwackzule:1984,
  author	= {J\"{u}rgen Guddat and Hansj\"{o}rg Wacker and W. Zulehner},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {79-96},
  title		= {On Imbedding and Parametric Optimization---A Concept of a
		  Globally Convergent Algorithm for Nonlinear Optimization
		  Problems},
  volume	= 21,
  year		= 1984
}

@Article{	  guyon2004result,
  title		= {Result analysis of the {NIPS} 2003 feature selection
		  challenge},
  author	= {Guyon, I. and Gunn, S. and Ben-Hur, A. and Dror, G.},
  journal	= {Advances Neural Inform. Processing Systems},
  volume	= 17,
  pages		= {545--552},
  year		= 2004,
  publisher	= {Citeseer}
}

###Article{	  guyon2004result,
  title		= {Result analysis of the nips 2003 feature selection
		  challenge},
  author	= {Guyon, I. and Gunn, S. and Ben-Hur, A. and Dror, G.},
  journal	= {Advances in Neural Information Processing Systems},
  volume	= {17},
  pages		= {545--552},
  year		= {2004},
  publisher	= {Citeseer}
}

@Article{	  haber:2012,
  author	= {E. Haber and M. Chung and F. J. Herrmann},
  journal	= siamopt,
  number	= 3,
  volume	= 22,
  pages		= {739--757},
  title		= {An effective method for parameter estimation with {PDE}
		  constraints with multiple right-hand sides},
  year		= 2012
}

###Article{	  haber:2012,
  author	= {E. Haber and M. Chung and F. J. Herrmann},
  journal	= siamopt,
  number	= 3,
  volume	= 22,
  pages		= {739--757},
  title		= {An effective method for parameter estimation with {PDE}
		  constraints with multiple right-hand sides},
  year		= 2012
}

###Article{	  haber:2012,
  author	= {E. Haber and M. Chung and F. J. Herrmann},
  journal	= siamopt,
  number	= 3,
  volume	= 22,
  pages		= {739--757},
  title		= {An effective method for parameter estimation with {PDE}
		  constraints with multiple right-hand sides},
  year		= 2012
}

@Article{	  hage:1987,
  author	= {W. W. Hager},
  issn		= {0022-3239},
  journal	= jota,
  mrclass	= {90C30 (49D07 49D29)},
  mrnumber	= {89j:90220},
  number	= 1,
  pages		= {37--71},
  title		= {Dual techniques for constrained optimization},
  volume	= 55,
  year		= 1987
}

###Article{	  hage:1987,
  author	= {W. W. Hager},
  journal	= jota,
  mrclass	= {90C30 (49D07 49D29)},
  mrnumber	= {89j:90220},
  number	= 1,
  pages		= {37--71},
  title		= {Dual techniques for constrained optimization},
  volume	= 55,
  year		= 1987
}

###Article{	  hage:1987,
  author	= {W. W. Hager},
  journal	= jota,
  mrclass	= {90C30 (49D07 49D29)},
  mrnumber	= {89j:90220},
  number	= 1,
  pages		= {37--71},
  title		= {Dual techniques for constrained optimization},
  volume	= 55,
  year		= 1987
}

@Article{	  haleyinzhang:2009,
  authors	= {Elaine T. Hale, Wotao Yin, Yin Zhang},
  journal	= {J. Comp. Math.},
  volume	= 28,
  number	= 2,
  year		= 2010,
  pages		= {170–194},
  doi		= {doi:10.4208/jcm.2009.10-m1007},
  title		= {Fixed-point continuation applied to compressed sensing:
		  implementation and numerical experiments}
}

###Article{	  haleyinzhang:2009,
  authors	= {Elaine T. Hale, Wotao Yin, Yin Zhang},
  journal	= {J. Comp. Math.},
  volume	= 28,
  number	= 2,
  year		= 2010,
  pages		= {170–194},
  doi		= {doi:10.4208/jcm.2009.10-m1007},
  title		= {Fixed-point continuation applied to compressed sensing:
		  implementation and numerical experiments}
}

###Article{	  haleyinzhang:2009,
  authors	= {Elaine T. Hale, Wotao Yin, Yin Zhang},
  journal	= {J. Comp. Math.},
  volume	= 28,
  number	= 2,
  year		= 2010,
  pages		= {170–194},
  doi		= {doi:10.4208/jcm.2009.10-m1007},
  title		= {Fixed-point continuation applied to compressed sensing:
		  implementation and numerical experiments}
}

@Book{		  hampel,
  author	= {Frank R. Hampel and Elvezio M. Ronchetti and Peter J.
		  Rousseeuw and Werner A. Stahel},
  publisher	= {Wiley},
  title		= {Robust Statistics: The Approach Based on Influence
		  Functions},
  edition	= {2nd},
  year		= 1986
}

###Book{	  hampel,
  author	= {Frank R. Hampel and Elvezio M. Ronchetti and Peter J.
		  Rousseeuw and Werner A. Stahel},
  publisher	= {Wiley},
  title		= {Robust Statistics: The Approach Based on Influence
		  Functions},
  edition	= {2nd},
  year		= 1986
}

###Book{	  hampel,
  author	= {Frank R. Hampel and Elvezio M. Ronchetti and Peter J.
		  Rousseeuw and Werner A. Stahel},
  publisher	= {Wiley},
  title		= {Robust Statistics: The Approach Based on Influence
		  Functions},
  edition	= {2nd},
  year		= 1986
}

@Article{	  hanmang:1979,
  author	= {S.-P. Han and O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathprog,
  pages		= {251--269},
  title		= {Exact penalty functions in nonlinear programming},
  volume	= 17,
  year		= 1979
}

###Article{	  hanmang:1979,
  author	= {S.-P. Han and O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathprog,
  pages		= {251--269},
  title		= {Exact penalty functions in nonlinear programming},
  volume	= 17,
  year		= 1979
}

###Article{	  hanmang:1979,
  author	= {S.-P. Han and O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathprog,
  pages		= {251--269},
  title		= {Exact penalty functions in nonlinear programming},
  volume	= 17,
  year		= 1979
}

@Article{	  hans:1992,
  abstract	= {When discrete ill-posed problems are analyzed and solved
		  by various numerical regularization techniques, a very
		  convenient way to display information about the regularized
		  solution is to plot the norm or seminorm of the solution
		  versus the norm of the residual vector. In particular, the
		  graph associated with Tikhonov regularization plays a
		  central role. The main purpose of this paper is to advocate
		  the use of this graph in the numerical treatment of
		  discrete ill-posed problems. The graph is characterized
		  quantitatively, and several important relations between
		  regularized solutions and the graph are derived. It is also
		  demonstrated that several methods for choosing the
		  regularization parameter are related to locating a
		  characteristic L-shaped ``corner'' of the graph. },
  author	= {Per Christian Hansen},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  keywords	= {discrete ill-posed problems, least squares, generalized
		  SVD, regularization},
  pages		= {561--580},
  title		= {Analysis of discrete ill-posed problems by means of the
		  {L}-curve},
  volume	= 34,
  year		= 1992
}

###Article{	  hans:1992,
  abstract	= {When discrete ill-posed problems are analyzed and solved
		  by various numerical regularization techniques, a very
		  convenient way to display information about the regularized
		  solution is to plot the norm or seminorm of the solution
		  versus the norm of the residual vector. In particular, the
		  graph associated with Tikhonov regularization plays a
		  central role. The main purpose of this paper is to advocate
		  the use of this graph in the numerical treatment of
		  discrete ill-posed problems. The graph is characterized
		  quantitatively, and several important relations between
		  regularized solutions and the graph are derived. It is also
		  demonstrated that several methods for choosing the
		  regularization parameter are related to locating a
		  characteristic L-shaped ``corner'' of the graph. },
  author	= {Per Christian Hansen},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  keywords	= {discrete ill-posed problems, least squares, generalized
		  SVD, regularization},
  pages		= {561--580},
  title		= {Analysis of discrete ill-posed problems by means of the
		  {L}-curve},
  volume	= 34,
  year		= 1992
}

###Article{	  hans:1992,
  abstract	= {When discrete ill-posed problems are analyzed and solved
		  by various numerical regularization techniques, a very
		  convenient way to display information about the regularized
		  solution is to plot the norm or seminorm of the solution
		  versus the norm of the residual vector. In particular, the
		  graph associated with Tikhonov regularization plays a
		  central role. The main purpose of this paper is to advocate
		  the use of this graph in the numerical treatment of
		  discrete ill-posed problems. The graph is characterized
		  quantitatively, and several important relations between
		  regularized solutions and the graph are derived. It is also
		  demonstrated that several methods for choosing the
		  regularization parameter are related to locating a
		  characteristic L-shaped ``corner'' of the graph. },
  author	= {Per Christian Hansen},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamreview,
  keywords	= {discrete ill-posed problems, least squares, generalized
		  SVD, regularization},
  pages		= {561--580},
  title		= {Analysis of discrete ill-posed problems by means of the
		  {L}-curve},
  volume	= 34,
  year		= 1992
}

@Book{		  hans:1998,
  address	= {Philadelphia},
  author	= {P. C. Hansen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= siampub,
  title		= {Rank-Deficient and Discrete Ill-Posed Problems},
  year		= 1998
}

###Book{	  hans:1998,
  address	= {Philadelphia},
  author	= {P. C. Hansen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= siampub,
  title		= {Rank-Deficient and Discrete Ill-Posed Problems},
  year		= 1998
}

###Book{	  hans:1998,
  address	= {Philadelphia},
  author	= {P. C. Hansen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= siampub,
  title		= {Rank-Deficient and Discrete Ill-Posed Problems},
  year		= 1998
}

@Article{	  hansolea:1993,
  abstract	= {Regularization algorithms are often used to produce
		  reasonable solutions to ill-posed problems. The L-curve is
		  a plot---for all valid regularization parameters---of the
		  size of the regularized solution versus the size of the
		  corresponding residual. Two main results are established.
		  First a unifying characterization of various regularization
		  methods is given and it is shown that the measurement of
		  ``size'' is dependent on the particular regularization
		  method chosen. For example, the 2-norm is appropriate for
		  Tikhonov regularization, but a 1-norm in the coordinate
		  system of the singular value decomposition (SVD) is
		  relevant to truncated SVD regularization. Second, a new
		  method is proposed for choosing the regularization
		  parameter based on the L-curve, and it is shown how this
		  method can be implemented efficiently. The method is
		  compared to generalized cross validation and this new
		  method is shown to be more robust in the presence of
		  correlated errors. },
  author	= {Per Christian Hansen and Dianne Prost O'Leary},
  date-modified	= {2008-01-01 21:28:50 -0800},
  journal	= siamscicomp,
  keywords	= {ill-posed problems, regularization, L-curve, parameter
		  choice, generalized cross validation, discrepancy
		  principle},
  local-url	= {file://localhost/Users/mpf/papers/HansOlea93.pdf},
  month		= {November},
  number	= 6,
  pages		= {1487--1503},
  title		= {The use of the {L}-curve in the regularization of discrete
		  ill-posed problems},
  volume	= 14,
  year		= 1993
}

###Article{	  hansolea:1993,
  abstract	= {Regularization algorithms are often used to produce
		  reasonable solutions to ill-posed problems. The L-curve is
		  a plot---for all valid regularization parameters---of the
		  size of the regularized solution versus the size of the
		  corresponding residual. Two main results are established.
		  First a unifying characterization of various regularization
		  methods is given and it is shown that the measurement of
		  ``size'' is dependent on the particular regularization
		  method chosen. For example, the 2-norm is appropriate for
		  Tikhonov regularization, but a 1-norm in the coordinate
		  system of the singular value decomposition (SVD) is
		  relevant to truncated SVD regularization. Second, a new
		  method is proposed for choosing the regularization
		  parameter based on the L-curve, and it is shown how this
		  method can be implemented efficiently. The method is
		  compared to generalized cross validation and this new
		  method is shown to be more robust in the presence of
		  correlated errors. },
  author	= {Per Christian Hansen and Dianne Prost O'Leary},
  date-modified	= {2008-01-01 21:28:50 -0800},
  journal	= siamscicomp,
  keywords	= {ill-posed problems, regularization, L-curve, parameter
		  choice, generalized cross validation, discrepancy
		  principle},
  local-url	= {file://localhost/Users/mpf/papers/HansOlea93.pdf},
  month		= {November},
  number	= 6,
  pages		= {1487--1503},
  title		= {The use of the {L}-curve in the regularization of discrete
		  ill-posed problems},
  volume	= 14,
  year		= 1993
}

###Article{	  hansolea:1993,
  abstract	= {Regularization algorithms are often used to produce
		  reasonable solutions to ill-posed problems. The L-curve is
		  a plot---for all valid regularization parameters---of the
		  size of the regularized solution versus the size of the
		  corresponding residual. Two main results are established.
		  First a unifying characterization of various regularization
		  methods is given and it is shown that the measurement of
		  ``size'' is dependent on the particular regularization
		  method chosen. For example, the 2-norm is appropriate for
		  Tikhonov regularization, but a 1-norm in the coordinate
		  system of the singular value decomposition (SVD) is
		  relevant to truncated SVD regularization. Second, a new
		  method is proposed for choosing the regularization
		  parameter based on the L-curve, and it is shown how this
		  method can be implemented efficiently. The method is
		  compared to generalized cross validation and this new
		  method is shown to be more robust in the presence of
		  correlated errors. },
  author	= {Per Christian Hansen and Dianne Prost O'Leary},
  date-modified	= {2008-01-01 21:28:50 -0800},
  journal	= siamscicomp,
  keywords	= {ill-posed problems, regularization, L-curve, parameter
		  choice, generalized cross validation, discrepancy
		  principle},
  local-url	= {file://localhost/Users/mpf/papers/HansOlea93.pdf},
  month		= {November},
  number	= 6,
  pages		= {1487--1503},
  title		= {The use of the {L}-curve in the regularization of discrete
		  ill-posed problems},
  volume	= 14,
  year		= 1993
}

@Article{	  hare:2009,
  title		= {A proximal method for identifying active manifolds},
  author	= {Hare, WL},
  journal	= compapplopt,
  volume	= 43,
  number	= 2,
  pages		= {295--306},
  year		= 2009,
  publisher	= {Springer}
}

###Article{	  hare:2009,
  title		= {A proximal method for identifying active manifolds},
  author	= {Hare, WL},
  journal	= compapplopt,
  volume	= 43,
  number	= 2,
  pages		= {295--306},
  year		= 2009,
  publisher	= {Springer}
}

@Book{		  hasttibsfrie:2001,
  author	= {T. Hastie and R. Tibshirani and J. Friedman},
  title		= {The Elements of Statistical Learning. Data mining,
		  inference, and prediction},
  publisher	= {Springer},
  year		= 2001
}

###Book{	  hasttibsfrie:2001,
  author	= {T. Hastie and R. Tibshirani and J. Friedman},
  title		= {The Elements of Statistical Learning. Data mining,
		  inference, and prediction},
  publisher	= {Springer},
  year		= 2001
}

###Book{	  hasttibsfrie:2001,
  author	= {T. Hastie and R. Tibshirani and J. Friedman},
  title		= {The Elements of Statistical Learning. Data mining,
		  inference, and prediction},
  publisher	= {Springer},
  year		= 2001
}

@InProceedings{	  hazankale:2012,
  title		= {Projection-free Online Learning},
  author	= {Hazan, Elad and Kale, Satyen},
  booktitle	= {Proceedings of the 29th International Conference on
		  Machine Learning (ICML-12)},
  pages		= {521--528},
  year		= 2012
}

###InProceedings{ hazankale:2012,
  title		= {Projection-free Online Learning},
  author	= {Hazan, Elad and Kale, Satyen},
  booktitle	= {Proceedings of the 29th International Conference on
		  Machine Learning (ICML-12)},
  pages		= {521--528},
  year		= 2012
}

@Article{	  hazapolashas:2005,
  author	= {Tamir Hazan and Simon Polak and Amnon Shashua},
  date-added	= {2007-09-13 22:03:22 -0700},
  date-modified	= {2007-10-22 16:32:33 -0700},
  doi		= {http://doi.ieeecomputersociety.org/10.1109/ICCV.2005.228},
  issn		= {1550-5499},
  journal	= {International Conference on Computer Vision},
  pages		= {50-57},
  publisher	= {IEEE Computer Society},
  title		= {Sparse Image Coding Using a 3D Non-Negative Tensor
		  Factorization},
  volume	= 1,
  year		= 2005
}

###Article{	  hazapolashas:2005,
  author	= {Tamir Hazan and Simon Polak and Amnon Shashua},
  date-added	= {2007-09-13 22:03:22 -0700},
  date-modified	= {2007-10-22 16:32:33 -0700},
  doi		= {http://doi.ieeecomputersociety.org/10.1109/ICCV.2005.228},
  journal	= {International Conference on Computer Vision},
  pages		= {50-57},
  publisher	= {IEEE Computer Society},
  title		= {Sparse Image Coding Using a 3D Non-Negative Tensor
		  Factorization},
  volume	= 1,
  year		= 2005
}

###Article{	  hazapolashas:2005,
  author	= {Tamir Hazan and Simon Polak and Amnon Shashua},
  date-added	= {2007-09-13 22:03:22 -0700},
  date-modified	= {2007-10-22 16:32:33 -0700},
  doi		= {http://doi.ieeecomputersociety.org/10.1109/ICCV.2005.228},
  journal	= {International Conference on Computer Vision},
  pages		= {50-57},
  publisher	= {IEEE Computer Society},
  title		= {Sparse Image Coding Using a 3D Non-Negative Tensor
		  Factorization},
  volume	= 1,
  year		= 2005
}

@Conference{	  heilschn:2005,
  author	= {M. Heiler and C. Schnorr},
  booktitle	= {Tenth IEEE International Conference on Computer Vision},
  date-added	= {2007-09-27 11:25:06 -0700},
  date-modified	= {2007-10-06 22:28:10 -0700},
  month		= {October},
  pages		= {1667-1674},
  title		= {Learning non-negative sparse image codes by convex
		  programming},
  volume	= {2},
  year		= {2005}
}

###Conference{	  heilschn:2005,
  author	= {M. Heiler and C. Schnorr},
  booktitle	= {Tenth IEEE International Conference on Computer Vision},
  date-added	= {2007-09-27 11:25:06 -0700},
  date-modified	= {2007-10-06 22:28:10 -0700},
  month		= {October},
  pages		= {1667-1674},
  title		= {Learning non-negative sparse image codes by convex
		  programming},
  volume	= {2},
  year		= {2005}
}

###Conference{	  heilschn:2005,
  author	= {M. Heiler and C. Schnorr},
  booktitle	= {Tenth IEEE International Conference on Computer Vision},
  date-added	= {2007-09-27 11:25:06 -0700},
  date-modified	= {2007-10-06 22:28:10 -0700},
  month		= {October},
  pages		= {1667-1674},
  title		= {Learning non-negative sparse image codes by convex
		  programming},
  volume	= {2},
  year		= {2005}
}

@Article{	  helmberg2014spectral,
  title		= {The spectral bundle method with second-order information},
  author	= {Helmberg, Christoph and Overton, Michael L and Rendl,
		  Franz},
  journal	= optimmeth,
  volume	= 29,
  number	= 4,
  pages		= {855--876},
  year		= 2014,
  publisher	= {Taylor \& Francis}
}

###Article{	  helmberg2014spectral,
  title		= {The spectral bundle method with second-order information},
  author	= {Helmberg, Christoph and Overton, Michael L and Rendl,
		  Franz},
  journal	= optimmeth,
  volume	= 29,
  number	= 4,
  pages		= {855--876},
  year		= 2014,
  publisher	= {Taylor \& Francis}
}

@Article{	  helmbergrendl:2000,
  title		= {A spectral bundle method for semidefinite programming},
  author	= {Helmberg, Christoph and Rendl, Franz},
  journal	= siamopt,
  volume	= 10,
  number	= 3,
  pages		= {673--696},
  year		= 2000,
  publisher	= {SIAM}
}

###Article{	  helmbergrendl:2000,
  title		= {A spectral bundle method for semidefinite programming},
  author	= {Helmberg, Christoph and Rendl, Franz},
  journal	= siamopt,
  volume	= 10,
  number	= 3,
  pages		= {673--696},
  year		= 2000,
  publisher	= {SIAM}
}

@InProceedings{	  hennherr:2005,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {SEG International Exposition and 75th Annual Meeting},
  title		= {Sparseness-constrained data continuation with frames:
		  Applications to missing traces and aliased signals in
		  {2/3-D}},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Conferences/SEG/hennenfent05seg.pdf},
  year		= 2005
}

###InProceedings{ hennherr:2005,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {SEG International Exposition and 75th Annual Meeting},
  title		= {Sparseness-constrained data continuation with frames:
		  Applications to missing traces and aliased signals in
		  {2/3-D}},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Conferences/SEG/hennenfent05seg.pdf},
  year		= 2005
}

###InProceedings{ hennherr:2005,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {SEG International Exposition and 75th Annual Meeting},
  title		= {Sparseness-constrained data continuation with frames:
		  Applications to missing traces and aliased signals in
		  {2/3-D}},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Conferences/SEG/hennenfent05seg.pdf},
  year		= 2005
}

@Article{	  hennherr:2007a,
  author	= {G. Hennenfent and F. J. Herrmann},
  title		= {Simply denoise: Wavefield reconstruction via jittered
		  undersampling},
  publisher	= {SEG},
  month		= {May-June},
  year		= 2008,
  journal	= geophysics,
  volume	= 73,
  number	= 3,
  pages		= {V19-V28},
  keywords	= {Fourier transforms; geophysical techniques; seismic waves;
		  seismology},
  url		= {http://link.aip.org/link/?GPY/73/V19/1},
  doi		= {10.1190/1.2841038}
}

###Article{	  hennherr:2007a,
  author	= {G. Hennenfent and F. J. Herrmann},
  title		= {Simply denoise: Wavefield reconstruction via jittered
		  undersampling},
  publisher	= {SEG},
  month		= {May-June},
  year		= 2008,
  journal	= geophysics,
  volume	= 73,
  number	= 3,
  pages		= {V19-V28},
  keywords	= {Fourier transforms; geophysical techniques; seismic waves;
		  seismology},
  url		= {http://link.aip.org/link/?GPY/73/V19/1},
  doi		= {10.1190/1.2841038}
}

###Article{	  hennherr:2007a,
  author	= {G. Hennenfent and F. J. Herrmann},
  title		= {Simply denoise: Wavefield reconstruction via jittered
		  undersampling},
  publisher	= {SEG},
  month		= {May-June},
  year		= 2008,
  journal	= geophysics,
  volume	= 73,
  number	= 3,
  pages		= {V19-V28},
  keywords	= {Fourier transforms; geophysical techniques; seismic waves;
		  seismology},
  url		= {http://link.aip.org/link/?GPY/73/V19/1},
  doi		= {10.1190/1.2841038}
}

@InProceedings{	  hennherr:2007b,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {SEG International Exposition and 77th Annual Meeting},
  date-added	= {2007-09-18 20:14:13 -0700},
  date-modified	= {2007-09-18 20:14:13 -0700},
  title		= {Random sampling: new insights into the reconstruction of
		  coarsely-sampled wavefields},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2007/hennenfent07seg.pdf},
  year		= 2007
}

###InProceedings{ hennherr:2007b,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {SEG International Exposition and 77th Annual Meeting},
  date-added	= {2007-09-18 20:14:13 -0700},
  date-modified	= {2007-09-18 20:14:13 -0700},
  title		= {Random sampling: new insights into the reconstruction of
		  coarsely-sampled wavefields},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2007/hennenfent07seg.pdf},
  year		= 2007
}

###InProceedings{ hennherr:2007b,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {SEG International Exposition and 77th Annual Meeting},
  date-added	= {2007-09-18 20:14:13 -0700},
  date-modified	= {2007-09-18 20:14:13 -0700},
  title		= {Random sampling: new insights into the reconstruction of
		  coarsely-sampled wavefields},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2007/hennenfent07seg.pdf},
  year		= 2007
}

@InProceedings{	  hennherr:2007c,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {EAGE 69th Conference \& Exhibition},
  date-added	= {2007-09-18 20:14:13 -0700},
  date-modified	= {2007-09-18 20:14:13 -0700},
  title		= {Irregular sampling: from aliasing to noise},
  url		= {http://slim.eos.ubc.ca/Publications/Private/Conferences/EAGE/2007/hennenfent07eage.pdf},
  year		= 2007
}

###InProceedings{ hennherr:2007c,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {EAGE 69th Conference \& Exhibition},
  date-added	= {2007-09-18 20:14:13 -0700},
  date-modified	= {2007-09-18 20:14:13 -0700},
  title		= {Irregular sampling: from aliasing to noise},
  url		= {http://slim.eos.ubc.ca/Publications/Private/Conferences/EAGE/2007/hennenfent07eage.pdf},
  year		= 2007
}

###InProceedings{ hennherr:2007c,
  author	= {G. Hennenfent and F. J. Herrmann},
  booktitle	= {EAGE 69th Conference \& Exhibition},
  date-added	= {2007-09-18 20:14:13 -0700},
  date-modified	= {2007-09-18 20:14:13 -0700},
  title		= {Irregular sampling: from aliasing to noise},
  url		= {http://slim.eos.ubc.ca/Publications/Private/Conferences/EAGE/2007/hennenfent07eage.pdf},
  year		= 2007
}

@Article{	  herrhenn:2008,
  title		= {Non-parametric seismic data recovery with curvelet
		  frames},
  author	= {F. J. Herrmann and Gilles Hennenfent},
  journal	= {Geophys. J. Int.},
  year		= 2008,
  volume	= 173,
  month		= {April},
  pages		= {233-248},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Journals/CRSI.pdf},
  abstract	= {Seismic data recovery from data with missing traces on
		  otherwise regular acquisition grids forms a crucial step in
		  the seismic processing flow. For instance, unsuccessful
		  recovery leads to imaging artifacts and to erroneous
		  predictions for the multiples, adversely affecting the
		  performance of multiple elimination. A non-parametric
		  transform-based recovery method is presented that exploits
		  the compression of seismic data volumes by recently
		  developed curvelet frames. The elements of this transform
		  are multidimensional and directional and locally resemble
		  wavefronts present in the data, which leads to a
		  compressible representation for seismic data. This
		  compression enables us to formulate a new curvelet-based
		  seismic data recovery algorithm through sparsity-promoting
		  inversion. The concept of sparsity- promoting inversion is
		  in itself not new to geophysics. However, the recent
		  insights from the field of compressed sensing are new since
		  they clearly identify the three main ingredients that go
		  into a successful formulation of a recovery problem, namely
		  a sparsifying transform, a sampling strategy that subdues
		  coherent aliases and a sparsity-promoting program that
		  recovers the largest entries of the curvelet-domain vector
		  while explaining the measurements. These concepts are
		  illustrated with a stylized experiment that stresses the
		  importance of the degree of compression by the sparsifying
		  transform. With these findings, a curvelet- based recovery
		  algorithms is developed, which recovers seismic wavefields
		  from seismic data volumes with large percentages of traces
		  missing. During this construction, we benefit from the main
		  three ingredients of compressive sampling, namely the
		  curvelet compression of seismic data, the existence of a
		  favorable sampling scheme and the formulation of a large-
		  scale sparsity-promoting solver based on a cooling method.
		  The recovery performs well on synthetic as well as real
		  data and performs better by virtue of the sparsifying
		  property of curvelets. Our results are applicable to other
		  areas such as global seismology.}
}

###Article{	  herrhenn:2008,
  title		= {Non-parametric seismic data recovery with curvelet
		  frames},
  author	= {F. J. Herrmann and Gilles Hennenfent},
  journal	= {Geophys. J. Int.},
  year		= 2008,
  volume	= 173,
  month		= {April},
  pages		= {233-248},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Journals/CRSI.pdf},
  abstract	= {Seismic data recovery from data with missing traces on
		  otherwise regular acquisition grids forms a crucial step in
		  the seismic processing flow. For instance, unsuccessful
		  recovery leads to imaging artifacts and to erroneous
		  predictions for the multiples, adversely affecting the
		  performance of multiple elimination. A non-parametric
		  transform-based recovery method is presented that exploits
		  the compression of seismic data volumes by recently
		  developed curvelet frames. The elements of this transform
		  are multidimensional and directional and locally resemble
		  wavefronts present in the data, which leads to a
		  compressible representation for seismic data. This
		  compression enables us to formulate a new curvelet-based
		  seismic data recovery algorithm through sparsity-promoting
		  inversion. The concept of sparsity- promoting inversion is
		  in itself not new to geophysics. However, the recent
		  insights from the field of compressed sensing are new since
		  they clearly identify the three main ingredients that go
		  into a successful formulation of a recovery problem, namely
		  a sparsifying transform, a sampling strategy that subdues
		  coherent aliases and a sparsity-promoting program that
		  recovers the largest entries of the curvelet-domain vector
		  while explaining the measurements. These concepts are
		  illustrated with a stylized experiment that stresses the
		  importance of the degree of compression by the sparsifying
		  transform. With these findings, a curvelet- based recovery
		  algorithms is developed, which recovers seismic wavefields
		  from seismic data volumes with large percentages of traces
		  missing. During this construction, we benefit from the main
		  three ingredients of compressive sampling, namely the
		  curvelet compression of seismic data, the existence of a
		  favorable sampling scheme and the formulation of a large-
		  scale sparsity-promoting solver based on a cooling method.
		  The recovery performs well on synthetic as well as real
		  data and performs better by virtue of the sparsifying
		  property of curvelets. Our results are applicable to other
		  areas such as global seismology.}
}

###Article{	  herrhenn:2008,
  title		= {Non-parametric seismic data recovery with curvelet
		  frames},
  author	= {F. J. Herrmann and Gilles Hennenfent},
  journal	= {Geophys. J. Int.},
  year		= 2008,
  volume	= 173,
  month		= {April},
  pages		= {233-248},
  url		= {http://slim.eos.ubc.ca/Publications/Public/Journals/CRSI.pdf},
  abstract	= {Seismic data recovery from data with missing traces on
		  otherwise regular acquisition grids forms a crucial step in
		  the seismic processing flow. For instance, unsuccessful
		  recovery leads to imaging artifacts and to erroneous
		  predictions for the multiples, adversely affecting the
		  performance of multiple elimination. A non-parametric
		  transform-based recovery method is presented that exploits
		  the compression of seismic data volumes by recently
		  developed curvelet frames. The elements of this transform
		  are multidimensional and directional and locally resemble
		  wavefronts present in the data, which leads to a
		  compressible representation for seismic data. This
		  compression enables us to formulate a new curvelet-based
		  seismic data recovery algorithm through sparsity-promoting
		  inversion. The concept of sparsity- promoting inversion is
		  in itself not new to geophysics. However, the recent
		  insights from the field of compressed sensing are new since
		  they clearly identify the three main ingredients that go
		  into a successful formulation of a recovery problem, namely
		  a sparsifying transform, a sampling strategy that subdues
		  coherent aliases and a sparsity-promoting program that
		  recovers the largest entries of the curvelet-domain vector
		  while explaining the measurements. These concepts are
		  illustrated with a stylized experiment that stresses the
		  importance of the degree of compression by the sparsifying
		  transform. With these findings, a curvelet- based recovery
		  algorithms is developed, which recovers seismic wavefields
		  from seismic data volumes with large percentages of traces
		  missing. During this construction, we benefit from the main
		  three ingredients of compressive sampling, namely the
		  curvelet compression of seismic data, the existence of a
		  favorable sampling scheme and the formulation of a large-
		  scale sparsity-promoting solver based on a cooling method.
		  The recovery performs well on synthetic as well as real
		  data and performs better by virtue of the sparsifying
		  property of curvelets. Our results are applicable to other
		  areas such as global seismology.}
}

@Article{	  hest:1969,
  author	= {M. R. Hestenes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= jota,
  number	= 5,
  pages		= {303--320},
  title		= {Multiplier and Gradient Methods},
  volume	= 4,
  year		= 1969
}

###Article{	  hest:1969,
  author	= {M. R. Hestenes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= jota,
  number	= 5,
  pages		= {303--320},
  title		= {Multiplier and Gradient Methods},
  volume	= 4,
  year		= 1969
}

###Article{	  hest:1969,
  author	= {M. R. Hestenes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= jota,
  number	= 5,
  pages		= {303--320},
  title		= {Multiplier and Gradient Methods},
  volume	= 4,
  year		= 1969
}

@TechReport{	  hgp08,
  author	= "Hoda, S. and Gilpin, A. and Pefla, J.",
  title		= "Smoothing techniques for computing Nash equilibria of
		  sequential games",
  institution	= "Carnegie Mellon University",
  address	= "Pittsburg",
  year		= 2008,
  month		= "March"
}

###TechReport{	  hgp08,
  author	= "Hoda, S. and Gilpin, A. and Pefla, J.",
  title		= "Smoothing techniques for computing Nash equilibria of
		  sequential games",
  institution	= "Carnegie Mellon University",
  address	= "Pittsburg",
  year		= 2008,
  month		= "March"
}

###TechReport{	  hgp08,
  author	= "Hoda, S. and Gilpin, A. and Pefla, J.",
  title		= "Smoothing techniques for computing Nash equilibria of
		  sequential games",
  institution	= "Carnegie Mellon University",
  address	= "Pittsburg",
  year		= 2008,
  month		= "March"
}

@Book{		  high:1998,
  address	= {Philadelphia},
  author	= {N. J. Higham},
  date-modified	= {2007-07-18 14:59:32 -0700},
  edition	= {Second},
  isbn		= {0-89871-420-6},
  pages		= {xvi+302},
  publisher	= siampub,
  title		= {Handbook of Writing for the Mathematical Sciences},
  year		= 1998
}

###Book{	  high:1998,
  address	= {Philadelphia},
  author	= {N. J. Higham},
  date-modified	= {2007-07-18 14:59:32 -0700},
  edition	= {Second},
  isbn		= {0-89871-420-6},
  pages		= {xvi+302},
  publisher	= siampub,
  title		= {Handbook of Writing for the Mathematical Sciences},
  year		= 1998
}

###Book{	  high:1998,
  address	= {Philadelphia},
  author	= {N. J. Higham},
  date-modified	= {2007-07-18 14:59:32 -0700},
  edition	= {Second},
  isbn		= {0-89871-420-6},
  pages		= {xvi+302},
  publisher	= siampub,
  title		= {Handbook of Writing for the Mathematical Sciences},
  year		= 1998
}

@Article{	  hintermuller2001proximal,
  title		= {A proximal bundle method based on approximate
		  subgradients},
  author	= {Hinterm{\"u}ller, Michael},
  journal	= {Computational Optimization and Applications},
  volume	= {20},
  number	= {3},
  pages		= {245--266},
  year		= {2001},
  publisher	= {Springer}
}

@Book{		  hiriart-urruty01,
  author	= {Hiriart-Urruty, J.-B. and Lemar\'echal, C.},
  title		= {Fundamentals of Convex Analysis},
  publisher	= {Springer},
  year		= 2001,
  keywords	= {Convex Analysis},
  address	= {New York, NY, USA}
}

###Book{	  hiriart-urruty01,
  author	= {Hiriart-Urruty, J.-B. and Lemar\'echal, C.},
  title		= {Fundamentals of Convex Analysis},
  publisher	= {Springer},
  year		= 2001,
  keywords	= {Convex Analysis},
  address	= {New York, NY, USA}
}

@Book{		  hockschi:1981,
  address	= {Berlin and New York},
  author	= {W. Hock and K. Schittkowski},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= springer,
  series	= {Lecture Notes in Economics and Mathematical Systems 187},
  title		= {Test Examples for Nonlinear Programming Codes},
  year		= 1981
}

###Book{	  hockschi:1981,
  address	= {Berlin and New York},
  author	= {W. Hock and K. Schittkowski},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= springer,
  series	= {Lecture Notes in Economics and Mathematical Systems 187},
  title		= {Test Examples for Nonlinear Programming Codes},
  year		= 1981
}

###Book{	  hockschi:1981,
  address	= {Berlin and New York},
  author	= {W. Hock and K. Schittkowski},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= springer,
  series	= {Lecture Notes in Economics and Mathematical Systems 187},
  title		= {Test Examples for Nonlinear Programming Codes},
  year		= 1981
}

@Article{	  hoeffding:1963,
  title		= {Probability inequalities for sums of bounded random
		  variables},
  author	= {Hoeffding, W.},
  journal	= {J. American Stat. Assoc.},
  volume	= 58,
  number	= 301,
  pages		= {13--30},
  year		= 1963
}

@Article{	  hong2012linear,
  title		= {On the linear convergence of the alternating direction
		  method of multipliers},
  author	= {Hong, Mingyi and Luo, Zhi-Quan},
  journal	= {arXiv preprint arXiv:1208.3922},
  year		= {2012}
}

@Article{	  horn:1979,
  author	= {J. Horning},
  journal	= {ACM SIGSOFT Software Engineering Notes},
  note		= {Cited in \cite{trop84}},
  number	= 4,
  pages		= 6,
  title		= {Note on Program Reliability},
  volume	= 4,
  year		= 1979
}

###Article{	  horn:1979,
  author	= {J. Horning},
  journal	= {ACM SIGSOFT Software Engineering Notes},
  note		= {Cited in \cite{trop84}},
  number	= 4,
  pages		= 6,
  title		= {Note on Program Reliability},
  volume	= 4,
  year		= 1979
}

###Article{	  horn:1979,
  author	= {J. Horning},
  journal	= {ACM SIGSOFT Software Engineering Notes},
  note		= {Cited in \cite{trop84}},
  number	= 4,
  pages		= 6,
  title		= {Note on Program Reliability},
  volume	= 4,
  year		= 1979
}

@Book{		  hornjohn:1985,
  author	= {R. A. Horn and Charles R. Johnson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  isbn		= {0-521-30586-1},
  pages		= {xiii+561},
  publisher	= cambridgepress,
  title		= {Matrix Analysis},
  year		= 1985
}

###Book{	  hornjohn:1985,
  author	= {R. A. Horn and Charles R. Johnson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  isbn		= {0-521-30586-1},
  pages		= {xiii+561},
  publisher	= cambridgepress,
  title		= {Matrix Analysis},
  year		= 1985
}

###Book{	  hornjohn:1985,
  author	= {R. A. Horn and Charles R. Johnson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  isbn		= {0-521-30586-1},
  pages		= {xiii+561},
  publisher	= cambridgepress,
  title		= {Matrix Analysis},
  year		= 1985
}

@Book{		  hornjohn:1991,
  author	= {R. A. Horn and Charles R. Johnson},
  isbn		= {0-521-30587-X},
  pages		= {viii+607},
  publisher	= cambridgepress,
  title		= {Topics in Matrix Analysis},
  year		= 1991
}

###Book{	  hornjohn:1991,
  author	= {R. A. Horn and Charles R. Johnson},
  isbn		= {0-521-30587-X},
  pages		= {viii+607},
  publisher	= cambridgepress,
  title		= {Topics in Matrix Analysis},
  year		= 1991
}

###Book{	  hornjohn:1991,
  author	= {R. A. Horn and Charles R. Johnson},
  isbn		= {0-521-30587-X},
  pages		= {viii+607},
  publisher	= cambridgepress,
  title		= {Topics in Matrix Analysis},
  year		= 1991
}

@Book{		  hosl00,
  title		= {Applied logistic regression},
  author	= {Hosmer, D.W. and Lemeshow, S.},
  year		= 2000,
  publisher	= {Wiley-Interscience}
}

###Book{	  hosl00,
  title		= {Applied logistic regression},
  author	= {Hosmer, D.W. and Lemeshow, S.},
  year		= 2000,
  publisher	= {Wiley-Interscience}
}

###Book{	  hosl00,
  title		= {Applied logistic regression},
  author	= {Hosmer, D.W. and Lemeshow, S.},
  year		= 2000,
  publisher	= {Wiley-Interscience}
}

@Article{	  huralp:2004,
  author	= {X. Hu and D. Ralph},
  journal	= jota,
  title		= {Convergence of a penalty method for mathematical programs
		  with complementarity constraints},
  volume	= {forthcoming},
  year		= 2004
}

###Article{	  huralp:2004,
  author	= {X. Hu and D. Ralph},
  journal	= jota,
  title		= {Convergence of a penalty method for mathematical programs
		  with complementarity constraints},
  volume	= {forthcoming},
  year		= 2004
}

###Article{	  huralp:2004,
  author	= {X. Hu and D. Ralph},
  journal	= jota,
  title		= {Convergence of a penalty method for mathematical programs
		  with complementarity constraints},
  volume	= {forthcoming},
  year		= 2004
}

@Article{	  hutchinson:1990,
  author	= {Hutchinson, M.F.},
  title		= {A stochastic estimator of the trace of the influence
		  matrix for laplacian smoothing splines},
  journal	= {Comm. Statist. Simulation Comput.},
  volume	= 19,
  number	= 2,
  pages		= {433-450},
  year		= 1990,
  doi		= {10.1080/03610919008812866},
  url		= {http://www.tandfonline.com/doi/abs/10.1080/03610919008812866},
  eprint	= {http://www.tandfonline.com/doi/pdf/10.1080/03610919008812866},
  abstract	= { An unbiased stochastic estimator of tr(I-A), where A is
		  the influence matrix associated with the calculation of
		  Laplacian smoothing splines, is described. The estimator is
		  similar to one recently developed by Girard but satisfies a
		  minimum variance criterion and does not require the
		  simulation of a standard normal variable. It uses instead
		  simulations of the discrete random variable which takes the
		  values 1, -1 each with probability 1/2. Bounds on the
		  variance of the estimator, similar to those established by
		  Girard, are obtained using elementary methods. The
		  estimator can be used to approximately minimize generalised
		  cross validation (GCV) when using discretized iterative
		  methods for fitting Laplacian smoothing splines to very
		  large data sets. Simulated examples show that the estimated
		  trace values, using either the estimator presented here or
		  the estimator of Girard, perform almost as well as the
		  exact values when applied to the minimization of GCV for n
		  as small as a few hundred, where n is the number of data
		  points. }
}

###Article{	  hutchinson:1990,
  author	= {Hutchinson, M.F.},
  title		= {A stochastic estimator of the trace of the influence
		  matrix for laplacian smoothing splines},
  journal	= {Comm. Statist. Simulation Comput.},
  volume	= 19,
  number	= 2,
  pages		= {433-450},
  year		= 1990,
  doi		= {10.1080/03610919008812866},
  url		= {http://www.tandfonline.com/doi/abs/10.1080/03610919008812866},
  eprint	= {http://www.tandfonline.com/doi/pdf/10.1080/03610919008812866},
  abstract	= { An unbiased stochastic estimator of tr(I-A), where A is
		  the influence matrix associated with the calculation of
		  Laplacian smoothing splines, is described. The estimator is
		  similar to one recently developed by Girard but satisfies a
		  minimum variance criterion and does not require the
		  simulation of a standard normal variable. It uses instead
		  simulations of the discrete random variable which takes the
		  values 1, -1 each with probability 1/2. Bounds on the
		  variance of the estimator, similar to those established by
		  Girard, are obtained using elementary methods. The
		  estimator can be used to approximately minimize generalised
		  cross validation (GCV) when using discretized iterative
		  methods for fitting Laplacian smoothing splines to very
		  large data sets. Simulated examples show that the estimated
		  trace values, using either the estimator presented here or
		  the estimator of Girard, perform almost as well as the
		  exact values when applied to the minimization of GCV for n
		  as small as a few hundred, where n is the number of data
		  points. }
}

###Article{	  hutchinson:1990,
  author	= {Hutchinson, M.F.},
  title		= {A stochastic estimator of the trace of the influence
		  matrix for laplacian smoothing splines},
  journal	= {Comm. Statist. Simulation Comput.},
  volume	= 19,
  number	= 2,
  pages		= {433-450},
  year		= 1990,
  doi		= {10.1080/03610919008812866},
  url		= {http://www.tandfonline.com/doi/abs/10.1080/03610919008812866},
  eprint	= {http://www.tandfonline.com/doi/pdf/10.1080/03610919008812866},
  abstract	= { An unbiased stochastic estimator of tr(I-A), where A is
		  the influence matrix associated with the calculation of
		  Laplacian smoothing splines, is described. The estimator is
		  similar to one recently developed by Girard but satisfies a
		  minimum variance criterion and does not require the
		  simulation of a standard normal variable. It uses instead
		  simulations of the discrete random variable which takes the
		  values 1, -1 each with probability 1/2. Bounds on the
		  variance of the estimator, similar to those established by
		  Girard, are obtained using elementary methods. The
		  estimator can be used to approximately minimize generalised
		  cross validation (GCV) when using discretized iterative
		  methods for fitting Laplacian smoothing splines to very
		  large data sets. Simulated examples show that the estimated
		  trace values, using either the estimator presented here or
		  the estimator of Girard, perform almost as well as the
		  exact values when applied to the minimization of GCV for n
		  as small as a few hundred, where n is the number of data
		  points. }
}

@TechReport{	  hyz07,
  address	= {Rice University, Houston, TX},
  author	= {Elaine Hale and Wotao Yin and Yin Zhang},
  institution	= {Department of Computational and Applied Mathematics},
  number	= {TR07-07},
  title		= {A Fixed-Point Continuation Method for $\ell1$-Regularized
		  Minimization with Applications to Compressed Sensing},
  year		= 2007
}

###TechReport{	  hyz07,
  address	= {Rice University, Houston, TX},
  author	= {Elaine Hale and Wotao Yin and Yin Zhang},
  institution	= {Department of Computational and Applied Mathematics},
  number	= {TR07-07},
  title		= {A Fixed-Point Continuation Method for $\ell1$-Regularized
		  Minimization with Applications to Compressed Sensing},
  year		= 2007
}

###TechReport{	  hyz07,
  address	= {Rice University, Houston, TX},
  author	= {Elaine Hale and Wotao Yin and Yin Zhang},
  institution	= {Department of Computational and Applied Mathematics},
  number	= {TR07-07},
  title		= {A Fixed-Point Continuation Method for $\ell1$-Regularized
		  Minimization with Applications to Compressed Sensing},
  year		= 2007
}

@Article{	  imhof:1961,
  title		= {Computing the distribution of quadratic forms in normal
		  variables},
  author	= {Imhof, J. P.},
  journal	= {Biometrika},
  pages		= {419--426},
  year		= 1961
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@Article{	  ishuscho:1986,
  author	= {V. S. Ishutkin and K. Sch\"{o}nefeld},
  journal	= computing,
  pages		= {151-169},
  title		= {On the Globalization of {Wilson}-type Optimization Methods
		  by Means of Generalized Reduced Gradient Methods},
  volume	= 37,
  year		= 1986
}

###Article{	  ishuscho:1986,
  author	= {V. S. Ishutkin and K. Sch\"{o}nefeld},
  journal	= computing,
  pages		= {151-169},
  title		= {On the Globalization of {Wilson}-type Optimization Methods
		  by Means of Generalized Reduced Gradient Methods},
  volume	= 37,
  year		= 1986
}

###Article{	  ishuscho:1986,
  author	= {V. S. Ishutkin and K. Sch\"{o}nefeld},
  journal	= computing,
  pages		= {151-169},
  title		= {On the Globalization of {Wilson}-type Optimization Methods
		  by Means of Generalized Reduced Gradient Methods},
  volume	= 37,
  year		= 1986
}

@Article{	  izmailovsolodov:2011,
  year		= 2011,
  journal	= mathprog,
  volume	= 126,
  number	= 2,
  doi		= {10.1007/s10107-009-0279-4},
  title		= {On attraction of linearly constrained Lagrangian methods
		  and of stabilized and quasi-{Newton} SQP methods to
		  critical multipliers},
  url		= {http://dx.doi.org/10.1007/s10107-009-0279-4},
  publisher	= {Springer-Verlag},
  keywords	= {Constrained optimization; Degenerate constraints;
		  Second-order sufficiency; Newton method; SQP; MINOS; SNOPT;
		  90C30; 90C33; 90C55; 65K05},
  author	= {Izmailov, A.F. and Solodov, M.V.},
  pages		= {231-257},
  language	= {English}
}

###Article{	  izmailovsolodov:2011,
  year		= 2011,
  journal	= mathprog,
  volume	= 126,
  number	= 2,
  doi		= {10.1007/s10107-009-0279-4},
  title		= {On attraction of linearly constrained Lagrangian methods
		  and of stabilized and quasi-{Newton} SQP methods to
		  critical multipliers},
  url		= {http://dx.doi.org/10.1007/s10107-009-0279-4},
  publisher	= {Springer-Verlag},
  keywords	= {Constrained optimization; Degenerate constraints;
		  Second-order sufficiency; Newton method; SQP; MINOS; SNOPT;
		  90C30; 90C33; 90C55; 65K05},
  author	= {Izmailov, A.F. and Solodov, M.V.},
  pages		= {231-257},
  language	= {English}
}

@InProceedings{	  jaggi:2013,
  title		= {Revisiting {Frank-Wolfe}: Projection-Free Sparse Convex
		  Optimization},
  author	= {Jaggi, Martin},
  booktitle	= {Proc. 30th Intern. Conf. Machine Learning (ICML-13)},
  pages		= {427--435},
  year		= 2013
}

###InProceedings{ jaggi:2013,
  title		= {Revisiting {Frank-Wolfe}: Projection-Free Sparse Convex
		  Optimization},
  author	= {Jaggi, Martin},
  booktitle	= {Proc. 30th Intern. Conf. Machine Learning (ICML-13)},
  pages		= {427--435},
  year		= 2013
}

@InProceedings{	  jenatton2010proximal,
  title		= {Proximal methods for sparse hierarchical dictionary
		  learning},
  author	= {Jenatton, Rodolphe and Mairal, Julien and Bach, Francis R
		  and Obozinski, Guillaume R},
  booktitle	= {Proc. 27th Intern. Confer. Machine Learning (ICML-10)},
  pages		= {487--494},
  year		= 2010
}

###InProceedings{ jenatton2010proximal,
  title		= {Proximal methods for sparse hierarchical dictionary
		  learning},
  author	= {Jenatton, Rodolphe and Mairal, Julien and Bach, Francis R
		  and Obozinski, Guillaume R},
  booktitle	= {Proceedings of the 27th International Conference on
		  Machine Learning (ICML-10)},
  pages		= {487--494},
  year		= 2010
}

@TechReport{	  jlns07,
  author	= "Juditsky, A. and Lan, G. and Nemirovski, A. and Shapiro,
		  A.",
  title		= "Stochastic Approximation approach to Stochastic
		  Programming",
  institution	= "to appear in SIAM J. Optim.",
  year		= 2007
}

###TechReport{	  jlns07,
  author	= "Juditsky, A. and Lan, G. and Nemirovski, A. and Shapiro,
		  A.",
  title		= "Stochastic Approximation approach to Stochastic
		  Programming",
  institution	= "to appear in SIAM J. Optim.",
  year		= 2007
}

###TechReport{	  jlns07,
  author	= "Juditsky, A. and Lan, G. and Nemirovski, A. and Shapiro,
		  A.",
  title		= "Stochastic Approximation approach to Stochastic
		  Programming",
  institution	= "to appear in SIAM J. Optim.",
  year		= 2007
}

@Article{	  jmlr:v16:trandihn15a,
  author	= {Quoc Tran-Dinh and Anastasios Kyrillidis and Volkan
		  Cevher},
  title		= {Composite Self-Concordant Minimization},
  journal	= {J. Machine Learning Research},
  year		= 2015,
  volume	= 16,
  pages		= {371-416}
}

@Book{		  jongjonktwil:1986,
  address	= {Frankfurt, Germany},
  author	= {H. Th. Jongen and P. Jonker and F. Twilt},
  publisher	= {Peter Lang Verlag},
  title		= {Nonlinear Optimization in $\mathcal{R}^n$ II:
		  Transversality, Flows, Parametric Aspects},
  year		= 1986
}

###Book{	  jongjonktwil:1986,
  address	= {Frankfurt, Germany},
  author	= {H. Th. Jongen and P. Jonker and F. Twilt},
  publisher	= {Peter Lang Verlag},
  title		= {Nonlinear Optimization in $\mathcal{R}^n$ II:
		  Transversality, Flows, Parametric Aspects},
  year		= 1986
}

###Book{	  jongjonktwil:1986,
  address	= {Frankfurt, Germany},
  author	= {H. Th. Jongen and P. Jonker and F. Twilt},
  publisher	= {Peter Lang Verlag},
  title		= {Nonlinear Optimization in $\mathcal{R}^n$ II:
		  Transversality, Flows, Parametric Aspects},
  year		= 1986
}

@Article{	  kanzqiqi:2003,
  author	= {C. Kanzow and H. Qi and L. Qi},
  journal	= jota,
  pages		= {333-345},
  title		= {On the minimum norm solution of linear programs},
  volume	= 116,
  year		= 2003
}

###Article{	  kanzqiqi:2003,
  author	= {C. Kanzow and H. Qi and L. Qi},
  journal	= jota,
  pages		= {333-345},
  title		= {On the minimum norm solution of linear programs},
  volume	= 116,
  year		= 2003
}

###Article{	  kanzqiqi:2003,
  author	= {C. Kanzow and H. Qi and L. Qi},
  journal	= jota,
  pages		= {333-345},
  title		= {On the minimum norm solution of linear programs},
  volume	= 116,
  year		= 2003
}

@Article{	  karl:1970,
  author	= {L. Karlovitz},
  journal	= {J. of Approx. Theory},
  title		= {Construction of nearest points in the $\ell^p$, $p$ even
		  and $\ell^1$ norms},
  volume	= 3,
  year		= 1970
}

###Article{	  karl:1970,
  author	= {L. Karlovitz},
  journal	= {J. of Approx. Theory},
  title		= {Construction of nearest points in the $\ell^p$, $p$ even
		  and $\ell^1$ norms},
  volume	= 3,
  year		= 1970
}

###Article{	  karl:1970,
  author	= {L. Karlovitz},
  journal	= {J. of Approx. Theory},
  title		= {Construction of nearest points in the $\ell^p$, $p$ even
		  and $\ell^1$ norms},
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###Book{	  kell:1995,
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  publisher	= {SIAM},
  series	= {Frontiers in applied mathematics},
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  volume	= 16,
  year		= 1995
}

###Book{	  kell:1995,
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  author	= {C. T. Kelley},
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@Article{	  kelley1960cutting,
  title		= {The cutting-plane method for solving convex programs},
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		  Mathematics},
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  year		= {1960},
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@Article{	  kellgoulwath:2000,
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  title		= {Constraint Preconditioning for Indefinite Linear Systems},
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  volume	= 21,
  year		= 2000
}

###Article{	  kellgoulwath:2000,
  author	= {C. Keller and N. I. M. Gould and A. J. Wathen},
  journal	= siammatrix,
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  pages		= {1300--1317},
  title		= {Constraint Preconditioning for Indefinite Linear Systems},
  url		= {citeseer.ist.psu.edu/keller00constraint.html},
  volume	= 21,
  year		= 2000
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###Article{	  kellgoulwath:2000,
  author	= {C. Keller and N. I. M. Gould and A. J. Wathen},
  journal	= siammatrix,
  number	= 4,
  pages		= {1300--1317},
  title		= {Constraint Preconditioning for Indefinite Linear Systems},
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  volume	= 21,
  year		= 2000
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@Article{	  kimkimkim:2006,
  title		= {Blockwise sparse regression},
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  year		= 2006
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###Article{	  kimkimkim:2006,
  title		= {Blockwise sparse regression},
  author	= {Kim, Y. and Kim, J. and Kim, Y.},
  journal	= {Statistica Sinica},
  volume	= 16,
  number	= 2,
  pages		= {375--390},
  year		= 2006
}

###Article{	  kimkimkim:2006,
  title		= {Blockwise sparse regression},
  author	= {Kim, Y. and Kim, J. and Kim, Y.},
  journal	= {Statistica Sinica},
  volume	= 16,
  number	= 2,
  pages		= {375--390},
  year		= 2006
}

@Article{	  kimkohlustboydgori:2007,
  author	= {S.-J. Kim and K. Koh and M. Lustig and S. Boyd and D.
		  Gorinevsky},
  title		= {An Interior-Point Method for Large-Scale {L1}-Regularized
		  Least Squares},
  journal	= {IEEE J. Sel. Top. Signal Process.},
  year		= 2007,
  volume	= 1,
  number	= 4,
  pages		= {606-617},
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}

###Article{	  kimkohlustboydgori:2007,
  author	= {S.-J. Kim and K. Koh and M. Lustig and S. Boyd and D.
		  Gorinevsky},
  title		= {An Interior-Point Method for Large-Scale {L1}-Regularized
		  Least Squares},
  journal	= {IEEE J. Sel. Top. Signal Process.},
  year		= 2007,
  volume	= 1,
  number	= 4,
  pages		= {606-617}
}

###Article{	  kimkohlustboydgori:2007,
  author	= {S.-J. Kim and K. Koh and M. Lustig and S. Boyd and D.
		  Gorinevsky},
  title		= {An Interior-Point Method for Large-Scale {L1}-Regularized
		  Least Squares},
  journal	= {IEEE J. Sel. Top. Signal Process.},
  year		= 2007,
  volume	= 1,
  number	= 4,
  pages		= {606-617}
}

@Article{	  kiw03,
  author	= "Kiwiel, K. C.",
  title		= "Convergence of Approximate and Incremental Subgradient
		  Methods for Convex Optimization",
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  year		= 2003,
  volume	= 14,
  page		= {807--840}
}

###Article{	  kiw03,
  author	= "Kiwiel, K. C.",
  title		= "Convergence of Approximate and Incremental Subgradient
		  Methods for Convex Optimization",
  journal	= siamopt,
  year		= 2003,
  volume	= 14,
  page		= {807--840}
}

###Article{	  kiw03,
  author	= "Kiwiel, K. C.",
  title		= "Convergence of Approximate and Incremental Subgradient
		  Methods for Convex Optimization",
  journal	= siamopt,
  year		= 2003,
  volume	= 14,
  page		= {807--840}
}

@Article{	  kiw07,
  author	= "Kiwiel, K. C.",
  title		= "On Linear-Time Algorithms for the Continuous Quadratic
		  Knapsack Problem",
  journal	= "Journal of Optimization Theory and Applications",
  year		= 2007,
  volume	= 134,
  page		= {549--554}
}

###Article{	  kiw07,
  author	= "Kiwiel, K. C.",
  title		= "On Linear-Time Algorithms for the Continuous Quadratic
		  Knapsack Problem",
  journal	= "Journal of Optimization Theory and Applications",
  year		= 2007,
  volume	= 134,
  page		= {549--554}
}

###Article{	  kiw07,
  author	= "Kiwiel, K. C.",
  title		= "On Linear-Time Algorithms for the Continuous Quadratic
		  Knapsack Problem",
  journal	= "Journal of Optimization Theory and Applications",
  year		= 2007,
  volume	= 134,
  page		= {549--554}
}

@Article{	  kiw95,
  author	= {Kiwiel, Krzysztof C.},
  coden		= {AMOMBN},
  fjournal	= {Applied Mathematics and Optimization. An International
		  Journal with Applications to Stochastics},
  issn		= {0095-4616},
  journal	= {Appl. Math. Optim.},
  keywords	= {Regularization, linear programs},
  mrclass	= {90C08 (49J52 90C25)},
  mrnumber	= {MR1340043 (96d:90051)},
  mrreviewer	= {Stefan Scholtes},
  number	= 3,
  pages		= {235--254},
  title		= {Finding normal solutions in piecewise linear programming},
  volume	= 32,
  year		= 1995
}

###Article{	  kiw95,
  author	= {Kiwiel, Krzysztof C.},
  coden		= {AMOMBN},
  fjournal	= {Applied Mathematics and Optimization. An International
		  Journal with Applications to Stochastics},
  journal	= {Appl. Math. Optim.},
  keywords	= {Regularization, linear programs},
  mrclass	= {90C08 (49J52 90C25)},
  mrnumber	= {MR1340043 (96d:90051)},
  mrreviewer	= {Stefan Scholtes},
  number	= 3,
  pages		= {235--254},
  title		= {Finding normal solutions in piecewise linear programming},
  volume	= 32,
  year		= 1995
}

###Article{	  kiw95,
  author	= {Kiwiel, Krzysztof C.},
  coden		= {AMOMBN},
  fjournal	= {Applied Mathematics and Optimization. An International
		  Journal with Applications to Stochastics},
  journal	= {Appl. Math. Optim.},
  keywords	= {Regularization, linear programs},
  mrclass	= {90C08 (49J52 90C25)},
  mrnumber	= {MR1340043 (96d:90051)},
  mrreviewer	= {Stefan Scholtes},
  number	= 3,
  pages		= {235--254},
  title		= {Finding normal solutions in piecewise linear programming},
  volume	= 32,
  year		= 1995
}

@Article{	  kiw95b,
  author	= {Kiwiel, Krzysztof C.},
  coden		= {LAAPAW},
  fjournal	= {Linear Algebra and its Applications},
  issn		= {0024-3795},
  journal	= {Linear Algebra Appl.},
  keywords	= {Regularization, linear programs},
  mrclass	= {90C08 (49M37 90C30)},
  mrnumber	= {MR1352835 (96g:90042)},
  mrreviewer	= {Stephan Dempe},
  pages		= {1--7},
  title		= {Iterative schemes for the least {$2$}-norm solution of
		  piecewise linear programs},
  volume	= 229,
  year		= 1995
}

###Article{	  kiw95b,
  author	= {Kiwiel, Krzysztof C.},
  coden		= {LAAPAW},
  fjournal	= {Linear Algebra and its Applications},
  journal	= {Linear Algebra Appl.},
  keywords	= {Regularization, linear programs},
  mrclass	= {90C08 (49M37 90C30)},
  mrnumber	= {MR1352835 (96g:90042)},
  mrreviewer	= {Stephan Dempe},
  pages		= {1--7},
  title		= {Iterative schemes for the least {$2$}-norm solution of
		  piecewise linear programs},
  volume	= 229,
  year		= 1995
}

###Article{	  kiw95b,
  author	= {Kiwiel, Krzysztof C.},
  coden		= {LAAPAW},
  fjournal	= {Linear Algebra and its Applications},
  journal	= {Linear Algebra Appl.},
  keywords	= {Regularization, linear programs},
  mrclass	= {90C08 (49M37 90C30)},
  mrnumber	= {MR1352835 (96g:90042)},
  mrreviewer	= {Stephan Dempe},
  pages		= {1--7},
  title		= {Iterative schemes for the least {$2$}-norm solution of
		  piecewise linear programs},
  volume	= 229,
  year		= 1995
}

@Article{	  kiw97,
  author	= "Kiwiel, K. C.",
  title		= "Proximal Minimization Methods with Generalized {Bregman}
		  Functions",
  journal	= "SIAM Journal on Control and Optimization",
  year		= 1997,
  volume	= 35,
  page		= {1142--1168}
}

###Article{	  kiw97,
  author	= "Kiwiel, K. C.",
  title		= "Proximal Minimization Methods with Generalized {Bregman}
		  Functions",
  journal	= "SIAM Journal on Control and Optimization",
  year		= 1997,
  volume	= 35,
  page		= {1142--1168}
}

###Article{	  kiw97,
  author	= "Kiwiel, K. C.",
  title		= "Proximal Minimization Methods with Generalized {Bregman}
		  Functions",
  journal	= "SIAM Journal on Control and Optimization",
  year		= 1997,
  volume	= 35,
  page		= {1142--1168}
}

@Article{	  kiwiel2006proximal,
  title		= {A proximal bundle method with approximate subgradient
		  linearizations},
  author	= {Kiwiel, Krzysztof C},
  journal	= {SIAM Journal on optimization},
  volume	= {16},
  number	= {4},
  pages		= {1007--1023},
  year		= {2006},
  publisher	= {SIAM}
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@Article{	  kiwiel2009bundle,
  title		= {Bundle methods for convex minimization with partially
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  author	= {Kiwiel, KC},
  journal	= {Comput. Optim. Appl., to appear},
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}

@TechReport{	  kkw08,
  author	= "Kim, S. and Kojima, M. and Waki, H.",
  title		= "Exploiting sparsity in SDP relaxation for sensor network
		  localization, report B-447",
  institution	= "Tokyo Institute of Technology",
  address	= "Tokyo",
  year		= 2008,
  month		= "October"
}

###TechReport{	  kkw08,
  author	= "Kim, S. and Kojima, M. and Waki, H.",
  title		= "Exploiting sparsity in SDP relaxation for sensor network
		  localization, report B-447",
  institution	= "Tokyo Institute of Technology",
  address	= "Tokyo",
  year		= 2008,
  month		= "October"
}

###TechReport{	  kkw08,
  author	= "Kim, S. and Kojima, M. and Waki, H.",
  title		= "Exploiting sparsity in SDP relaxation for sensor network
		  localization, report B-447",
  institution	= "Tokyo Institute of Technology",
  address	= "Tokyo",
  year		= 2008,
  month		= "October"
}

@Book{		  klenke2008probability,
  title		= {Probability theory: a comprehensive course},
  author	= {Klenke, Achim},
  year		= {2008},
  publisher	= {Springer Verlag},
  address	= {London}
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@Article{	  kleywegt2002sample,
  title		= {{The sample average approximation method for stochastic
		  discrete optimization}},
  author	= {Kleywegt, A.J. and Shapiro, A. and Homem-de-Mello, T.},
  journal	= siamopt,
  volume	= 12,
  number	= 2,
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  year		= 2002,
  publisher	= {Citeseer}
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###Article{	  kleywegt2002sample,
  title		= {{The sample average approximation method for stochastic
		  discrete optimization}},
  author	= {Kleywegt, A.J. and Shapiro, A. and Homem-de-Mello, T.},
  journal	= siamopt,
  volume	= 12,
  number	= 2,
  pages		= {479--502},
  year		= 2002,
  publisher	= {Citeseer}
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###Article{	  kleywegt2002sample,
  title		= {{The sample average approximation method for stochastic
		  discrete optimization}},
  author	= {Kleywegt, A.J. and Shapiro, A. and Homem-de-Mello, T.},
  journal	= siamopt,
  volume	= 12,
  number	= 2,
  pages		= {479--502},
  year		= 2002,
  publisher	= {Citeseer}
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@Article{	  kly:2007,
  title		= {A primal-dual active-set method for non-negativity
		  constrained total variation deblurring problems},
  author	= {Krishnan, Dilip and Lin, Ping and Yip, Andy M},
  journal	= ieeetransimproc,
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###Article{	  kly:2007,
  title		= {A primal-dual active-set method for non-negativity
		  constrained total variation deblurring problems},
  author	= {Krishnan, Dilip and Lin, Ping and Yip, Andy M},
  journal	= ieeetransimproc,
  volume	= 16,
  number	= 11,
  pages		= {2766--2777},
  year		= 2007,
  publisher	= {IEEE}
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@Article{	  kobesuhl:2007,
  author	= {A. Koberstein and U. H. Suhl},
  title		= {Progress in the dual simplex method for large scale {LP}
		  problems: practical dual phase 1 algorithms},
  journal	= {J. Comp. Optim. Appl.},
  year		= 2007,
  volume	= 37,
  number	= 1,
  pages		= {49-65},
  month		= {May},
  doi		= {10.1007/s10589-007-9022-3}
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###Article{	  kobesuhl:2007,
  author	= {A. Koberstein and U. H. Suhl},
  title		= {Progress in the dual simplex method for large scale {LP}
		  problems: practical dual phase 1 algorithms},
  journal	= compapplopt,
  year		= 2007,
  volume	= 37,
  number	= 1,
  pages		= {49-65},
  month		= {May},
  doi		= {10.1007/s10589-007-9022-3}
}

###Article{	  kobesuhl:2007,
  author	= {A. Koberstein and U. H. Suhl},
  title		= {Progress in the dual simplex method for large scale {LP}
		  problems: practical dual phase 1 algorithms},
  journal	= compapplopt,
  year		= 2007,
  volume	= 37,
  number	= 1,
  pages		= {49-65},
  month		= {May},
  doi		= {10.1007/s10589-007-9022-3}
}

@TechReport{	  kold:2006,
  author	= {T. G. Kolda},
  institution	= {Sandia National Laboratories},
  local-url	= {file://localhost/Users/mpf/papers/Kold06.pdf},
  title		= {Multilinear operators for higher-order decompositions},
  year		= 2006
}

###TechReport{	  kold:2006,
  author	= {T. G. Kolda},
  institution	= {Sandia National Laboratories},
  local-url	= {file://localhost/Users/mpf/papers/Kold06.pdf},
  title		= {Multilinear operators for higher-order decompositions},
  year		= 2006
}

###TechReport{	  kold:2006,
  author	= {T. G. Kolda},
  institution	= {Sandia National Laboratories},
  local-url	= {file://localhost/Users/mpf/papers/Kold06.pdf},
  title		= {Multilinear operators for higher-order decompositions},
  year		= 2006
}

@Article{	  koldbade:2009,
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  title		= {Tensor Decompositions and Applications},
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  volume	= 51,
  number	= 3,
  pages		= {455--500},
  doi		= {10.1137/07070111X}
}

###Article{	  koldbade:2009,
  author	= {Tamara G. Kolda and Brett W. Bader},
  title		= {Tensor Decompositions and Applications},
  journal	= siamreview,
  month		= {September},
  year		= 2009,
  volume	= 51,
  number	= 3,
  pages		= {455--500},
  doi		= {10.1137/07070111X}
}

###Article{	  koldbade:2009,
  author	= {Tamara G. Kolda and Brett W. Bader},
  title		= {Tensor Decompositions and Applications},
  journal	= siamreview,
  month		= {September},
  year		= 2009,
  volume	= 51,
  number	= 3,
  pages		= {455--500},
  doi		= {10.1137/07070111X}
}

@TechReport{	  kpw06,
  author	= "Krislock, N. and Piccialli, V. and Wolkowicz, H.",
  title		= "Robust semidefinite programming approaches for sensor
		  network localization with anchors",
  institution	= "Department of Combinatorics and Optimization, University
		  of Waterloo",
  address	= "Waterloo",
  year		= 2006,
  month		= "May"
}

###TechReport{	  kpw06,
  author	= "Krislock, N. and Piccialli, V. and Wolkowicz, H.",
  title		= "Robust semidefinite programming approaches for sensor
		  network localization with anchors",
  institution	= "Department of Combinatorics and Optimization, University
		  of Waterloo",
  address	= "Waterloo",
  year		= 2006,
  month		= "May"
}

###TechReport{	  kpw06,
  author	= "Krislock, N. and Piccialli, V. and Wolkowicz, H.",
  title		= "Robust semidefinite programming approaches for sensor
		  network localization with anchors",
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		  of Waterloo",
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@Book{		  kull:1959,
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###Book{	  kull:1959,
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###Book{	  kull:1959,
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@Misc{		  l1-bregman,
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###Misc{	  l1-bregman,
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###Article{	  laiyin:2013,
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@Article{	  langford:2009:sol:1577069.1577097,
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###Article{	  langford:2009:sol:1577069.1577097,
  author	= {Langford, John and Li, Lihong and Zhang, Tong},
  title		= {Sparse Online Learning via Truncated Gradient},
  journal	= {J. Mach. Learn. Res.},
  issue_date	= {12/1/2009},
  volume	= 10,
  month		= jun,
  year		= 2009,
  issn		= {1532-4435},
  pages		= {777--801},
  numpages	= 25,
  url		= {http://dl.acm.org/citation.cfm?id=1577069.1577097},
  acmid		= 1577097,
  publisher	= {JMLR.org}
}

###Article{	  langford:2009:sol:1577069.1577097,
  author	= {Langford, John and Li, Lihong and Zhang, Tong},
  title		= {Sparse Online Learning via Truncated Gradient},
  journal	= {J. Mach. Learn. Res.},
  issue_date	= {12/1/2009},
  volume	= 10,
  month		= jun,
  year		= 2009,
  issn		= {1532-4435},
  pages		= {777--801},
  numpages	= 25,
  url		= {http://dl.acm.org/citation.cfm?id=1577069.1577097},
  acmid		= 1577097,
  publisher	= {JMLR.org}
}

@Misc{		  lars:2001,
  author	= {R. M. Larsen},
  date-modified	= {2007-07-18 14:59:32 -0700},
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		  in {L}anczos bidiagnalization},
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###Misc{	  lars:2001,
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###TechReport{	  leyf:2002,
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  month		= {Februaru},
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###TechReport{	  leyf:2002,
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@TechReport{	  leyf:2005,
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###TechReport{	  leyf:2005,
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  month		= {September},
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  title		= {A note on multiobjective optimization and complementarity
		  constraints},
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###TechReport{	  leyf:2005,
  address	= {Illinois},
  author	= {S. Leyffer},
  institution	= {Mathematics and Computer Science Division, Argonne
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  keywords	= {multiobjective, complementarity, pareto},
  month		= {September},
  number	= {ANL/MCS-P1290-0905},
  title		= {A note on multiobjective optimization and complementarity
		  constraints},
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###Article{	  lin:2007,
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  year		= 1999
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###Article{	  linmore:1999,
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###Article{	  linmore:1999b,
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###Article{	  linmore:1999b,
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@TechReport{	  liusun:2001,
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  date-modified	= {2007-07-18 14:59:32 -0700},
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  title		= {Generalized stationary points and an interior point method
		  for mathematical programs with equilibrium constraints},
  year		= 2001
}

###TechReport{	  liusun:2001,
  author	= {Xinwei Liu and Jie Sun},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {National University of Singapore},
  title		= {Generalized stationary points and an interior point method
		  for mathematical programs with equilibrium constraints},
  year		= 2001
}

###TechReport{	  liusun:2001,
  author	= {Xinwei Liu and Jie Sun},
  date-modified	= {2007-07-18 14:59:32 -0700},
  institution	= {National University of Singapore},
  title		= {Generalized stationary points and an interior point method
		  for mathematical programs with equilibrium constraints},
  year		= 2001
}

@TechReport{	  liv08,
  author	= "Liu, Z. and Vandenberghe, L.",
  title		= "Interior-point method for nuclear norm approximation with
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  institution	= "Electrical Engineering Department, UCLA",
  address	= "Los Angeles",
  year		= 2008
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###TechReport{	  liv08,
  author	= "Liu, Z. and Vandenberghe, L.",
  title		= "Interior-point method for nuclear norm approximation with
		  application to system identification",
  institution	= "Electrical Engineering Department, UCLA",
  address	= "Los Angeles",
  year		= 2008
}

###TechReport{	  liv08,
  author	= "Liu, Z. and Vandenberghe, L.",
  title		= "Interior-point method for nuclear norm approximation with
		  application to system identification",
  institution	= "Electrical Engineering Department, UCLA",
  address	= "Los Angeles",
  year		= 2008
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@TechReport{	  llm06,
  author	= "G. Lan and Z. Lu and R. D. C. Monteiro",
  title		= "Primal-dual first-order methods with $O(1/e)O(1/\epsilon)$
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  institution	= "School of Industrial and Systems Engineering, Georgia
		  Institute of Technology",
  address	= "Atlanta",
  year		= 2006,
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###TechReport{	  llm06,
  author	= "G. Lan and Z. Lu and R. D. C. Monteiro",
  title		= "Primal-dual first-order methods with $O(1/e)O(1/\epsilon)$
		  iteration-complexity for cone programming",
  institution	= "School of Industrial and Systems Engineering, Georgia
		  Institute of Technology",
  address	= "Atlanta",
  year		= 2006,
  month		= "December"
}

###TechReport{	  llm06,
  author	= "G. Lan and Z. Lu and R. D. C. Monteiro",
  title		= "Primal-dual first-order methods with $O(1/e)O(1/\epsilon)$
		  iteration-complexity for cone programming",
  institution	= "School of Industrial and Systems Engineering, Georgia
		  Institute of Technology",
  address	= "Atlanta",
  year		= 2006,
  month		= "December"
}

@TechReport{	  lmy08,
  author	= "Lu, Z. and Monteiro, R. D. C. and Yuan, M.",
  title		= "Convex optimization methods for dimension reduction and
		  coefficient estimation in multivariate linear regression",
  institution	= "School of Industrial and Systems Engineering, Georgia
		  Institute of Technology",
  address	= "Atlanta",
  year		= 2008,
  month		= "January",
  note		= "revised March 2009"
}

###TechReport{	  lmy08,
  author	= "Lu, Z. and Monteiro, R. D. C. and Yuan, M.",
  title		= "Convex optimization methods for dimension reduction and
		  coefficient estimation in multivariate linear regression",
  institution	= "School of Industrial and Systems Engineering, Georgia
		  Institute of Technology",
  address	= "Atlanta",
  year		= 2008,
  month		= "January",
  note		= "revised March 2009"
}

###TechReport{	  lmy08,
  author	= "Lu, Z. and Monteiro, R. D. C. and Yuan, M.",
  title		= "Convex optimization methods for dimension reduction and
		  coefficient estimation in multivariate linear regression",
  institution	= "School of Industrial and Systems Engineering, Georgia
		  Institute of Technology",
  address	= "Atlanta",
  year		= 2008,
  month		= "January",
  note		= "revised March 2009"
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  year		= 2007,
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###Article{	  lnm07,
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  year		= 2007,
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  title		= {The ubiquitous {Kroneckar} product},
  volume	= 123,
  year		= 2000
}

###Article{	  loan:2000,
  author	= {C. F. Van Loan},
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  date-modified	= {2007-09-27 14:54:56 -0700},
  journal	= compapplmath,
  month		= {November},
  pages		= {85-100},
  title		= {The ubiquitous {Kroneckar} product},
  volume	= 123,
  year		= 2000
}

###Article{	  loan:2000,
  author	= {C. F. Van Loan},
  date-added	= {2007-09-27 14:46:49 -0700},
  date-modified	= {2007-09-27 14:54:56 -0700},
  journal	= compapplmath,
  month		= {November},
  pages		= {85-100},
  title		= {The ubiquitous {Kroneckar} product},
  volume	= 123,
  year		= 2000
}

@Book{		  lohr1999sampling,
  title		= {{Sampling: design and analysis}},
  author	= {Lohr, Sharon L.},
  year		= 1999,
  publisher	= {Duxbury Press},
  address	= {Pacific Grove}
}

###Book{	  lohr1999sampling,
  title		= {{Sampling: design and analysis}},
  author	= {Lohr, Sharon L.},
  year		= 1999,
  publisher	= {Duxbury Press},
  address	= {Pacific Grove}
}

###Book{	  lohr1999sampling,
  title		= {{Sampling: design and analysis}},
  author	= {Lohr, Sharon L.},
  year		= 1999,
  publisher	= {Duxbury Press},
  address	= {Pacific Grove}
}

@Article{	  lorenz2011constructing,
  title		= {Constructing test instances for basis pursuit denoising},
  author	= {Lorenz, D. A.},
  journal	= {arXiv preprint arXiv:1103.2897},
  year		= {2011}
}

@TechReport{	  louzengosheryin:2014,
  title		= {A Weighted Difference of Anisotropic and Isotropic Total
		  Variation Model for Image Processing},
  author	= {Lou, Yifei and Zeng, Tieyong and Osher, Stanley and Xin,
		  Jack},
  institution	= {Department of Mathematics, UCLA},
  year		= 2014
}

###TechReport{	  louzengosheryin:2014,
  title		= {A Weighted Difference of Anisotropic and Isotropic Total
		  Variation Model for Image Processing},
  author	= {Lou, Yifei and Zeng, Tieyong and Osher, Stanley and Xin,
		  Jack},
  institution	= {Department of Mathematics, UCLA},
  year		= 2014
}

@InCollection{	  lovasz:1983,
  year		= 1983,
  booktitle	= {Mathematical Programming The State of the Art},
  editor	= {Bachem, Achim and Korte, Bernhard and Grötschel, Martin},
  doi		= {10.1007/978-3-642-68874-4_10},
  title		= {Submodular functions and convexity},
  publisher	= {Springer},
  author	= {Lov\`asz, L.},
  pages		= {235-257},
  language	= {English}
}

###InCollection{  lovasz:1983,
  year		= 1983,
  booktitle	= {Mathematical Programming The State of the Art},
  editor	= {Bachem, Achim and Korte, Bernhard and Grötschel, Martin},
  doi		= {10.1007/978-3-642-68874-4_10},
  title		= {Submodular functions and convexity},
  publisher	= {Springer},
  author	= {Lov\`asz, L.},
  pages		= {235-257},
  language	= {English}
}

@Book{		  lovemorrweso:1988,
  author	= {R. F. Love and J. G. Morris and G. O. Wesolowsky},
  title		= {Facilities location: models and methods},
  year		= 1988,
  address	= {New York},
  publisher	= {North-Holland}
}

###Book{	  lovemorrweso:1988,
  author	= {R. F. Love and J. G. Morris and G. O. Wesolowsky},
  title		= {Facilities location: models and methods},
  year		= 1988,
  address	= {New York},
  publisher	= {North-Holland}
}

###Book{	  lovemorrweso:1988,
  author	= {R. F. Love and J. G. Morris and G. O. Wesolowsky},
  title		= {Facilities location: models and methods},
  year		= 1988,
  address	= {New York},
  publisher	= {North-Holland}
}

@Article{	  lst:2011,
  author	= {Liu, Yong-Jin and Sun, Defeng and Toh, Kim-Chuan},
  affiliation	= {Faculty of Science, Shenyang Aerospace University, 110136
		  Shenyang, People’s Republic of China},
  title		= {An implementable proximal point algorithmic framework for
		  nuclear norm minimization},
  journal	= {Mathematical Programming},
  publisher	= {Springer Berlin / Heidelberg},
  issn		= {0025-5610},
  keyword	= {Mathematics and Statistics},
  pages		= {1-38},
  url		= {http://dx.doi.org/10.1007/s10107-010-0437-8},
  note		= {10.1007/s10107-010-0437-8},
  year		= 2011
}

@TechReport{	  lu08,
  author	= "Lu, Z.",
  title		= "Smooth optimization approach for sparse covariance
		  selection",
  institution	= "Department of Mathematics, Simon Fraser University",
  address	= "Burnaby",
  year		= 2008,
  month		= "January",
  note		= "submitted to SIAM J. Optim."
}

###TechReport{	  lu08,
  author	= "Lu, Z.",
  title		= "Smooth optimization approach for sparse covariance
		  selection",
  institution	= "Department of Mathematics, Simon Fraser University",
  address	= "Burnaby",
  year		= 2008,
  month		= "January",
  note		= "submitted to SIAM J. Optim."
}

###TechReport{	  lu08,
  author	= "Lu, Z.",
  title		= "Smooth optimization approach for sparse covariance
		  selection",
  institution	= "Department of Mathematics, Simon Fraser University",
  address	= "Burnaby",
  year		= 2008,
  month		= "January",
  note		= "submitted to SIAM J. Optim."
}

@Article{	  luci:1987,
  author	= {Lucidi, S.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  fjournal	= {Journal of Optimization Theory and Applications},
  journal	= jota,
  number	= 1,
  pages		= {103--117},
  title		= {A new result in the theory and computation of the
		  least-norm solution of a linear program},
  volume	= 55,
  year		= 1987
}

###Article{	  luci:1987,
  author	= {Lucidi, S.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  fjournal	= {Journal of Optimization Theory and Applications},
  journal	= jota,
  number	= 1,
  pages		= {103--117},
  title		= {A new result in the theory and computation of the
		  least-norm solution of a linear program},
  volume	= 55,
  year		= 1987
}

###Article{	  luci:1987,
  author	= {Lucidi, S.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  fjournal	= {Journal of Optimization Theory and Applications},
  journal	= jota,
  number	= 1,
  pages		= {103--117},
  title		= {A new result in the theory and computation of the
		  least-norm solution of a linear program},
  volume	= 55,
  year		= 1987
}

@Article{	  luci:1987b,
  author	= {Lucidi, S.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  fjournal	= {Optimization. A Journal of Mathematical Programming and
		  Operations Research},
  journal	= {Optimization},
  number	= 6,
  pages		= {809--823},
  title		= {A finite algorithm for the least two-norm solution of a
		  linear program},
  volume	= 18,
  year		= 1987
}

###Article{	  luci:1987b,
  author	= {Lucidi, S.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  fjournal	= {Optimization. A Journal of Mathematical Programming and
		  Operations Research},
  journal	= {Optimization},
  number	= 6,
  pages		= {809--823},
  title		= {A finite algorithm for the least two-norm solution of a
		  linear program},
  volume	= 18,
  year		= 1987
}

###Article{	  luci:1987b,
  author	= {Lucidi, S.},
  date-modified	= {2007-07-18 14:59:31 -0700},
  fjournal	= {Optimization. A Journal of Mathematical Programming and
		  Operations Research},
  journal	= {Optimization},
  number	= 6,
  pages		= {809--823},
  title		= {A finite algorithm for the least two-norm solution of a
		  linear program},
  volume	= 18,
  year		= 1987
}

@Article{	  ludo:2007,
  author	= {Y. M. Lu and M. N. Do},
  title		= {Multidimensional directional filter banks and
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  volume	= 16,
  number	= 4,
  month		= {April},
  pages		= {918--931},
  year		= 2007
}

###Article{	  ludo:2007,
  author	= {Y. M. Lu and M. N. Do},
  title		= {Multidimensional directional filter banks and
		  surfacelets},
  journal	= ieeetransimproc,
  volume	= 16,
  number	= 4,
  month		= {April},
  pages		= {918--931},
  year		= 2007
}

###Article{	  ludo:2007,
  author	= {Y. M. Lu and M. N. Do},
  title		= {Multidimensional directional filter banks and
		  surfacelets},
  journal	= ieeetransimproc,
  volume	= 16,
  number	= 4,
  month		= {April},
  pages		= {918--931},
  year		= 2007
}

@Book{		  luen:1984,
  author	= {David G. Luenberger},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {Second},
  publisher	= addisonwesley,
  title		= {Linear and Nonlinear Programming},
  year		= 1984
}

###Book{	  luen:1984,
  author	= {David G. Luenberger},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {Second},
  publisher	= addisonwesley,
  title		= {Linear and Nonlinear Programming},
  year		= 1984
}

###Book{	  luen:1984,
  author	= {David G. Luenberger},
  date-modified	= {2007-07-18 14:59:33 -0700},
  edition	= {Second},
  publisher	= addisonwesley,
  title		= {Linear and Nonlinear Programming},
  year		= 1984
}

@Book{		  luenberger2008linear,
  title		= {{Linear and nonlinear programming}},
  author	= {Luenberger, D.G. and Ye, Y.},
  year		= 2008,
  publisher	= {Springer Verlag}
}

###Book{	  luenberger2008linear,
  title		= {{Linear and nonlinear programming}},
  author	= {Luenberger, D.G. and Ye, Y.},
  year		= 2008,
  publisher	= {Springer Verlag}
}

###Book{	  luenberger2008linear,
  title		= {{Linear and nonlinear programming}},
  author	= {Luenberger, D.G. and Ye, Y.},
  year		= 2008,
  publisher	= {Springer Verlag}
}

@Article{	  luo1993error,
  title		= {{Error bounds and convergence analysis of feasible descent
		  methods: A general approach}},
  author	= {Luo, Z.Q. and Tseng, P.},
  journal	= {Ann. Oper. Res.},
  volume	= 46,
  number	= 1,
  pages		= {157--178},
  year		= 1993
}

###Article{	  luo1993error,
  title		= {{Error bounds and convergence analysis of feasible descent
		  methods: A general approach}},
  author	= {Luo, Z.Q. and Tseng, P.},
  journal	= {Ann. Oper. Res.},
  volume	= 46,
  number	= 1,
  pages		= {157--178},
  year		= 1993
}

###Article{	  luo1993error,
  title		= {{Error bounds and convergence analysis of feasible descent
		  methods: A general approach}},
  author	= {Luo, Z.Q. and Tseng, P.},
  journal	= {Ann. Oper. Res.},
  volume	= 46,
  number	= 1,
  pages		= {157--178},
  year		= 1993
}

@Article{	  luo91,
  author	= "Luo, Z. Q.",
  title		= "On the convergence of the LMS algorithm with adaptive
		  learning rate for linear feedforward networks",
  journal	= "Neural Computation",
  year		= 1991,
  volume	= 3,
  page		= {226--245}
}

###Article{	  luo91,
  author	= "Luo, Z. Q.",
  title		= "On the convergence of the LMS algorithm with adaptive
		  learning rate for linear feedforward networks",
  journal	= "Neural Computation",
  year		= 1991,
  volume	= 3,
  page		= {226--245}
}

###Article{	  luo91,
  author	= "Luo, Z. Q.",
  title		= "On the convergence of the LMS algorithm with adaptive
		  learning rate for linear feedforward networks",
  journal	= "Neural Computation",
  year		= 1991,
  volume	= 3,
  page		= {226--245}
}

@Article{	  luopang:1994,
  author	= {Z.-Q. Luo and J.-S. Pang},
  journal	= mathprog,
  pages		= {1--28},
  title		= {Error bounds for analytic systems and their applications},
  volume	= 67,
  year		= 1994
}

###Article{	  luopang:1994,
  author	= {Z.-Q. Luo and J.-S. Pang},
  journal	= mathprog,
  pages		= {1--28},
  title		= {Error bounds for analytic systems and their applications},
  volume	= 67,
  year		= 1994
}

###Article{	  luopang:1994,
  author	= {Z.-Q. Luo and J.-S. Pang},
  journal	= mathprog,
  pages		= {1--28},
  title		= {Error bounds for analytic systems and their applications},
  volume	= 67,
  year		= 1994
}

@Proceedings{	  luopang:2000,
  editor	= {Z.-Q. Luo and J.-S. Pang},
  journal	= mathprog,
  key		= {LuP00},
  pages		= {221--410},
  title		= {Error bounds in mathematical programming, {\rm Math.\
		  Program.}},
  volume	= 88,
  year		= 2000
}

###Proceedings{	  luopang:2000,
  editor	= {Z.-Q. Luo and J.-S. Pang},
  journal	= mathprog,
  key		= {LuP00},
  pages		= {221--410},
  title		= {Error bounds in mathematical programming, {\rm Math.\
		  Program.}},
  volume	= 88,
  year		= 2000
}

###Proceedings{	  luopang:2000,
  editor	= {Z.-Q. Luo and J.-S. Pang},
  journal	= mathprog,
  key		= {LuP00},
  pages		= {221--410},
  title		= {Error bounds in mathematical programming, {\rm Math.\
		  Program.}},
  volume	= 88,
  year		= 2000
}

@Book{		  luopangralph:1996,
  author	= {Z.-Q. Luo and J.-S. Pang and D. Ralph},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Cambridge University Press},
  title		= {Mathematical Programs with Equilibrium Constraints},
  year		= 1996
}

###Book{	  luopangralph:1996,
  author	= {Z.-Q. Luo and J.-S. Pang and D. Ralph},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Cambridge University Press},
  title		= {Mathematical Programs with Equilibrium Constraints},
  year		= 1996
}

###Book{	  luopangralph:1996,
  author	= {Z.-Q. Luo and J.-S. Pang and D. Ralph},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Cambridge University Press},
  title		= {Mathematical Programs with Equilibrium Constraints},
  year		= 1996
}

@TechReport{	  luosturzhan:1997,
  address	= {Erasmus University, Rotterdam, The Netherlands},
  author	= {Z.-Q. Luo and J. F. Sturm and S. Zhang},
  institution	= {Econometric Institute},
  month		= {April},
  number	= {No. 9719/A},
  title		= {Duality results for conic convex programming},
  type		= {Econometric Institute Report},
  year		= 1997
}

###TechReport{	  luosturzhan:1997,
  address	= {Erasmus University, Rotterdam, The Netherlands},
  author	= {Z.-Q. Luo and J. F. Sturm and S. Zhang},
  institution	= {Econometric Institute},
  month		= {April},
  number	= {No. 9719/A},
  title		= {Duality results for conic convex programming},
  type		= {Econometric Institute Report},
  year		= 1997
}

###TechReport{	  luosturzhan:1997,
  address	= {Erasmus University, Rotterdam, The Netherlands},
  author	= {Z.-Q. Luo and J. F. Sturm and S. Zhang},
  institution	= {Econometric Institute},
  month		= {April},
  number	= {No. 9719/A},
  title		= {Duality results for conic convex programming},
  type		= {Econometric Institute Report},
  year		= 1997
}

@Article{	  lustdonopaul:2007a,
  author	= {M. Lustig and D. L. Donoho and J. M. Pauly},
  title		= {Sparse {MRI}: The application of compressed sensing for
		  rapid {MR} imaging},
  journal	= {Mag. Resonance Med.},
  volume	= 58,
  number	= 6,
  pages		= {1182--1195},
  month		= {December},
  year		= 2007,
  doi		= {10.1002/mrm.21391}
}

###Article{	  lustdonopaul:2007a,
  author	= {M. Lustig and D. L. Donoho and J. M. Pauly},
  title		= {Sparse {MRI}: The application of compressed sensing for
		  rapid {MR} imaging},
  journal	= {Mag. Resonance Med.},
  volume	= 58,
  number	= 6,
  pages		= {1182--1195},
  month		= {December},
  year		= 2007,
  doi		= {10.1002/mrm.21391}
}

###Article{	  lustdonopaul:2007a,
  author	= {M. Lustig and D. L. Donoho and J. M. Pauly},
  title		= {Sparse {MRI}: The application of compressed sensing for
		  rapid {MR} imaging},
  journal	= {Mag. Resonance Med.},
  volume	= 58,
  number	= 6,
  pages		= {1182--1195},
  month		= {December},
  year		= 2007,
  doi		= {10.1002/mrm.21391}
}

@Misc{		  lustdonopaul:2007b,
  author	= {M. Lustig and D. L. Donoho and J. M. Pauly},
  date-added	= {2007-09-14 10:54:08 -0700},
  date-modified	= {2007-09-14 14:52:20 -0700},
  howpublished	= {\url{http://www.stanford.edu/~mlustig/SparseMRI.html}},
  title		= {Sparse {MRI} {Matlab} code},
  year		= 2007
}

###Misc{	  lustdonopaul:2007b,
  author	= {M. Lustig and D. L. Donoho and J. M. Pauly},
  date-added	= {2007-09-14 10:54:08 -0700},
  date-modified	= {2007-09-14 14:52:20 -0700},
  howpublished	= {\url{http://www.stanford.edu/~mlustig/SparseMRI.html}},
  title		= {Sparse {MRI} {Matlab} code},
  year		= 2007
}

###Misc{	  lustdonopaul:2007b,
  author	= {M. Lustig and D. L. Donoho and J. M. Pauly},
  date-added	= {2007-09-14 10:54:08 -0700},
  date-modified	= {2007-09-14 14:52:20 -0700},
  howpublished	= {\url{http://www.stanford.edu/~mlustig/SparseMRI.html}},
  title		= {Sparse {MRI} {Matlab} code},
  year		= 2007
}

@Article{	  lustdonosantpaul:2007,
  author	= {M. Lustig and D. L. Donoho and J. M. Santos and J. M.
		  Pauly},
  title		= {Compressed Sensing {MRI}},
  journal	= {IEEE Signal Process. Mag.},
  volume	= 25,
  number	= 2,
  pages		= {72--82},
  month		= {March},
  year		= 2007
}

###Article{	  lustdonosantpaul:2007,
  author	= {M. Lustig and D. L. Donoho and J. M. Santos and J. M.
		  Pauly},
  title		= {Compressed Sensing {MRI}},
  journal	= {IEEE Signal Process. Mag.},
  volume	= 25,
  number	= 2,
  pages		= {72--82},
  month		= {March},
  year		= 2007
}

###Article{	  lustdonosantpaul:2007,
  author	= {M. Lustig and D. L. Donoho and J. M. Santos and J. M.
		  Pauly},
  title		= {Compressed Sensing {MRI}},
  journal	= {IEEE Signal Process. Mag.},
  volume	= 25,
  number	= 2,
  pages		= {72--82},
  month		= {March},
  year		= 2007
}

@Article{	  lut92,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the linear convergence of descent methods for convex
		  essentially smooth minimization",
  journal	= siamcontrol,
  year		= 1992,
  volume	= 30,
  page		= {408--425}
}

###Article{	  lut92,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the linear convergence of descent methods for convex
		  essentially smooth minimization",
  journal	= siamcontrol,
  year		= 1992,
  volume	= 30,
  page		= {408--425}
}

###Article{	  lut92,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the linear convergence of descent methods for convex
		  essentially smooth minimization",
  journal	= siamcontrol,
  year		= 1992,
  volume	= 30,
  page		= {408--425}
}

@Article{	  lut93,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the convergence rate of dual ascent methods for
		  linearly constrained convex minimization",
  journal	= "Math. Oper. Res.",
  year		= 1993,
  volume	= 18,
  page		= {846--867}
}

###Article{	  lut93,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the convergence rate of dual ascent methods for
		  linearly constrained convex minimization",
  journal	= "Math. Oper. Res.",
  year		= 1993,
  volume	= 18,
  page		= {846--867}
}

###Article{	  lut93,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the convergence rate of dual ascent methods for
		  linearly constrained convex minimization",
  journal	= "Math. Oper. Res.",
  year		= 1993,
  volume	= 18,
  page		= {846--867}
}

@Article{	  lut94,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "Error bounds and convergence analysis of feasible descent
		  methods: a general approach",
  journal	= "Annals of Operations Research",
  year		= 1993,
  volume	= 46,
  page		= {157--178}
}

###Article{	  lut94,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "Error bounds and convergence analysis of feasible descent
		  methods: a general approach",
  journal	= "Annals of Operations Research",
  year		= 1993,
  volume	= 46,
  page		= {157--178}
}

###Article{	  lut94,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "Error bounds and convergence analysis of feasible descent
		  methods: a general approach",
  journal	= "Annals of Operations Research",
  year		= 1993,
  volume	= 46,
  page		= {157--178}
}

@Article{	  lut94b,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the rate of convergence of a distributed asynchronous
		  routing algorithm",
  journal	= "Automatic Control, IEEE Transactions",
  year		= 1994,
  volume	= 39,
  page		= {1123--1129}
}

###Article{	  lut94b,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the rate of convergence of a distributed asynchronous
		  routing algorithm",
  journal	= "Automatic Control, IEEE Transactions",
  year		= 1994,
  volume	= 39,
  page		= {1123--1129}
}

###Article{	  lut94b,
  author	= "Luo, Z. Q. and Tseng, P.",
  title		= "On the rate of convergence of a distributed asynchronous
		  routing algorithm",
  journal	= "Automatic Control, IEEE Transactions",
  year		= 1994,
  volume	= 39,
  page		= {1123--1129}
}

@TechReport{	  lwy04,
  author	= "Liang, T.-C. and Wang, T.-C. and Ye, Y.",
  title		= "A gradient search method to round the semidefinite
		  programming relaxation solution for ad hoc wireless sensor
		  network localization",
  institution	= "Electrical Engineering, Stanford University",
  year		= 2004,
  month		= "October"
}

###TechReport{	  lwy04,
  author	= "Liang, T.-C. and Wang, T.-C. and Ye, Y.",
  title		= "A gradient search method to round the semidefinite
		  programming relaxation solution for ad hoc wireless sensor
		  network localization",
  institution	= "Electrical Engineering, Stanford University",
  year		= 2004,
  month		= "October"
}

###TechReport{	  lwy04,
  author	= "Liang, T.-C. and Wang, T.-C. and Ye, Y.",
  title		= "A gradient search method to round the semidefinite
		  programming relaxation solution for ad hoc wireless sensor
		  network localization",
  institution	= "Electrical Engineering, Stanford University",
  year		= 2004,
  month		= "October"
}

@Article{	  ma2012alternating,
  title		= {Alternating proximal gradient method for convex
		  minimization},
  author	= {Ma, Shiqian},
  journal	= {Journal of Scientific Computing},
  pages		= {1--27},
  year		= {2012},
  publisher	= {Springer}
}

@Misc{		  magoldchen:2009,
  author	= {Shiqian Ma and Donald Goldfarb and Lifeng Chen},
  title		= {Fixed point and {Bregman} iterative methods for matrix
		  rank minimization},
  url		= {http://arxiv.org/abs/0905.1643},
  howpublished	= {arXiv 0905.1643},
  month		= {May},
  year		= 2009
}

@MastersThesis{	  mali:2003,
  address	= {Cambridge, MA},
  author	= {Dmitry M. Malioutov},
  month		= {July},
  school	= {Dept. Electrical Engineering, Massachusetts Institute of
		  Technology},
  title		= {A Sparse Signal Reconstruction Perspective for Source
		  Localization with Sensor Arrays},
  year		= 2003
}

###MastersThesis{ mali:2003,
  address	= {Cambridge, MA},
  author	= {Dmitry M. Malioutov},
  month		= {July},
  school	= {Dept. Electrical Engineering, Massachusetts Institute of
		  Technology},
  title		= {A Sparse Signal Reconstruction Perspective for Source
		  Localization with Sensor Arrays},
  year		= 2003
}

###MastersThesis{ mali:2003,
  address	= {Cambridge, MA},
  author	= {Dmitry M. Malioutov},
  month		= {July},
  school	= {Dept. Electrical Engineering, Massachusetts Institute of
		  Technology},
  title		= {A Sparse Signal Reconstruction Perspective for Source
		  Localization with Sensor Arrays},
  year		= 2003
}

@Article{	  malicetiwill:2005,
  abstract	= {We present a source localization method based on a sparse
		  representation of sensor measurements with an overcomplete
		  basis composed of samples from the array manifold. We
		  enforce sparsity by imposing penalties based on the
		  $\ell_1$-norm. A number of recent theoretical results on
		  sparsifying properties of $\ell_1$ penalties justify this
		  choice. Explicitly enforcing the sparsity of the
		  representation is motivated by a desire to obtain a sharp
		  estimate of the spatial spectrum that exhibits
		  super-resolution. We propose to use the singular value
		  decomposition (SVD) of the data matrix to summarize
		  multiple time or frequency samples. Our formulation leads
		  to an optimization problem, which we solve efficiently in a
		  second-order cone (SOC) programming framework by an
		  interior point implementation. We propose a grid refinement
		  method to mitigate the effects of limiting estimates to a
		  grid of spatial locations and introduce an automatic
		  selection criterion for the regularization parameter
		  involved in our approach. We demonstrate the effectiveness
		  of the method on simulated data by plots of spatial spectra
		  and by comparing the estimator variance to the
		  Crame\'{e}r-Rao bound (CRB). We observe that our approach
		  has a number of advantages over other source localization
		  techniques, including increased resolution, improved
		  robustness to noise, limitations in data quantity, and
		  correlation of the sources, as well as not requiring an
		  accurate initialization. },
  author	= {Dmitry Malioutov and M\"{u}jad \c{C}etin and Alan S.
		  Willsky},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= ieeetranssigproc,
  keywords	= {Direction-of-arrival estimation, overcomplete
		  representation, sensor array processing, source
		  localization, sparse representation, superresolution},
  month		= {August},
  number	= 8,
  pages		= {3010--3022},
  title		= {A sparse signal reconstruction perspective for source
		  localization with sensor arrays},
  volume	= 53,
  year		= 2005
}

###Article{	  malicetiwill:2005,
  abstract	= {We present a source localization method based on a sparse
		  representation of sensor measurements with an overcomplete
		  basis composed of samples from the array manifold. We
		  enforce sparsity by imposing penalties based on the
		  $\ell_1$-norm. A number of recent theoretical results on
		  sparsifying properties of $\ell_1$ penalties justify this
		  choice. Explicitly enforcing the sparsity of the
		  representation is motivated by a desire to obtain a sharp
		  estimate of the spatial spectrum that exhibits
		  super-resolution. We propose to use the singular value
		  decomposition (SVD) of the data matrix to summarize
		  multiple time or frequency samples. Our formulation leads
		  to an optimization problem, which we solve efficiently in a
		  second-order cone (SOC) programming framework by an
		  interior point implementation. We propose a grid refinement
		  method to mitigate the effects of limiting estimates to a
		  grid of spatial locations and introduce an automatic
		  selection criterion for the regularization parameter
		  involved in our approach. We demonstrate the effectiveness
		  of the method on simulated data by plots of spatial spectra
		  and by comparing the estimator variance to the
		  Crame\'{e}r-Rao bound (CRB). We observe that our approach
		  has a number of advantages over other source localization
		  techniques, including increased resolution, improved
		  robustness to noise, limitations in data quantity, and
		  correlation of the sources, as well as not requiring an
		  accurate initialization. },
  author	= {Dmitry Malioutov and M\"{u}jad \c{C}etin and Alan S.
		  Willsky},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= ieeetranssigproc,
  keywords	= {Direction-of-arrival estimation, overcomplete
		  representation, sensor array processing, source
		  localization, sparse representation, superresolution},
  month		= {August},
  number	= 8,
  pages		= {3010--3022},
  title		= {A sparse signal reconstruction perspective for source
		  localization with sensor arrays},
  volume	= 53,
  year		= 2005
}

###Article{	  malicetiwill:2005,
  abstract	= {We present a source localization method based on a sparse
		  representation of sensor measurements with an overcomplete
		  basis composed of samples from the array manifold. We
		  enforce sparsity by imposing penalties based on the
		  $\ell_1$-norm. A number of recent theoretical results on
		  sparsifying properties of $\ell_1$ penalties justify this
		  choice. Explicitly enforcing the sparsity of the
		  representation is motivated by a desire to obtain a sharp
		  estimate of the spatial spectrum that exhibits
		  super-resolution. We propose to use the singular value
		  decomposition (SVD) of the data matrix to summarize
		  multiple time or frequency samples. Our formulation leads
		  to an optimization problem, which we solve efficiently in a
		  second-order cone (SOC) programming framework by an
		  interior point implementation. We propose a grid refinement
		  method to mitigate the effects of limiting estimates to a
		  grid of spatial locations and introduce an automatic
		  selection criterion for the regularization parameter
		  involved in our approach. We demonstrate the effectiveness
		  of the method on simulated data by plots of spatial spectra
		  and by comparing the estimator variance to the
		  Crame\'{e}r-Rao bound (CRB). We observe that our approach
		  has a number of advantages over other source localization
		  techniques, including increased resolution, improved
		  robustness to noise, limitations in data quantity, and
		  correlation of the sources, as well as not requiring an
		  accurate initialization. },
  author	= {Dmitry Malioutov and M\"{u}jad \c{C}etin and Alan S.
		  Willsky},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= ieeetranssigproc,
  keywords	= {Direction-of-arrival estimation, overcomplete
		  representation, sensor array processing, source
		  localization, sparse representation, superresolution},
  month		= {August},
  number	= 8,
  pages		= {3010--3022},
  title		= {A sparse signal reconstruction perspective for source
		  localization with sensor arrays},
  volume	= 53,
  year		= 2005
}

@InProceedings{	  malicetiwill:2005b,
  author	= {D. M. Malioutov and M. \c{C}etin and A. S. Willsky},
  booktitle	= {{IEEE} International Conference on Acoustics, Speech, and
		  Signal Processing},
  location	= {Philadelphia, PA},
  month		= {March},
  pages		= {733--736},
  title		= {Homotopy Continuation for Sparse Signal Representation},
  volume	= 5,
  year		= 2005
}

###InProceedings{ malicetiwill:2005b,
  author	= {D. M. Malioutov and M. \c{C}etin and A. S. Willsky},
  booktitle	= {{IEEE} International Conference on Acoustics, Speech, and
		  Signal Processing},
  location	= {Philadelphia, PA},
  month		= {March},
  pages		= {733--736},
  title		= {Homotopy Continuation for Sparse Signal Representation},
  volume	= 5,
  year		= 2005
}

###InProceedings{ malicetiwill:2005b,
  author	= {D. M. Malioutov and M. \c{C}etin and A. S. Willsky},
  booktitle	= {{IEEE} International Conference on Acoustics, Speech, and
		  Signal Processing},
  location	= {Philadelphia, PA},
  month		= {March},
  pages		= {733--736},
  title		= {Homotopy Continuation for Sparse Signal Representation},
  volume	= 5,
  year		= 2005
}

@InProceedings{	  malisangwill:2008,
  author	= {Dmitry Malioutov and Sujay Sanghavi and Alan Willsky},
  title		= {Compressed sensing with sequential observations},
  year		= 2008,
  month		= {April},
  booktitle	= {Proceedings of the International Conference on Acoustics,
		  Speech, and Signal Processing},
  publisher	= {IEEE Signal Processing Society}
}

###InProceedings{ malisangwill:2008,
  author	= {Dmitry Malioutov and Sujay Sanghavi and Alan Willsky},
  title		= {Compressed sensing with sequential observations},
  year		= 2008,
  month		= {April},
  booktitle	= {Proceedings of the International Conference on Acoustics,
		  Speech, and Signal Processing},
  publisher	= {IEEE Signal Processing Society}
}

###InProceedings{ malisangwill:2008,
  author	= {Dmitry Malioutov and Sujay Sanghavi and Alan Willsky},
  title		= {Compressed sensing with sequential observations},
  year		= 2008,
  month		= {April},
  booktitle	= {Proceedings of the International Conference on Acoustics,
		  Speech, and Signal Processing},
  publisher	= {IEEE Signal Processing Society}
}

@Book{		  mallat:1999,
  author	= {S. Mallat},
  title		= {A Wavlet Tour of Signal Processing},
  publisher	= {Academic Press},
  year		= 1999
}

###Book{	  mallat:1999,
  author	= {S. Mallat},
  title		= {A Wavlet Tour of Signal Processing},
  publisher	= {Academic Press},
  year		= 1999
}

###Book{	  mallat:1999,
  author	= {S. Mallat},
  title		= {A Wavlet Tour of Signal Processing},
  publisher	= {Academic Press},
  year		= 1999
}

@Article{	  mam99,
  author	= "Mangasarian, O. L. and Musicant, D. R.",
  title		= "Successive over relaxation for support vector machines",
  journal	= "IEEE Trans. Neural Networks",
  year		= 1999,
  volume	= 10,
  page		= {1032--1037}
}

###Article{	  mam99,
  author	= "Mangasarian, O. L. and Musicant, D. R.",
  title		= "Successive over relaxation for support vector machines",
  journal	= "IEEE Trans. Neural Networks",
  year		= 1999,
  volume	= 10,
  page		= {1032--1037}
}

###Article{	  mam99,
  author	= "Mangasarian, O. L. and Musicant, D. R.",
  title		= "Successive over relaxation for support vector machines",
  journal	= "IEEE Trans. Neural Networks",
  year		= 1999,
  volume	= 10,
  page		= {1032--1037}
}

@Article{	  man84,
  author	= "Mangasarian, O. L.",
  title		= "Sparsity-Preserving SOR Algorithms for Separable Quadratic
		  and Linear Programming",
  journal	= "Comnput. Oper. Res.",
  year		= 1984,
  volume	= 11,
  page		= {105--112}
}

###Article{	  man84,
  author	= "Mangasarian, O. L.",
  title		= "Sparsity-Preserving SOR Algorithms for Separable Quadratic
		  and Linear Programming",
  journal	= "Comnput. Oper. Res.",
  year		= 1984,
  volume	= 11,
  page		= {105--112}
}

###Article{	  man84,
  author	= "Mangasarian, O. L.",
  title		= "Sparsity-Preserving SOR Algorithms for Separable Quadratic
		  and Linear Programming",
  journal	= "Comnput. Oper. Res.",
  year		= 1984,
  volume	= 11,
  page		= {105--112}
}

@Article{	  man93,
  author	= "Mangasarian, O. L.",
  title		= "Mathematical programming in neural networks",
  journal	= "ORSA J. Comput.",
  year		= 1993,
  volume	= 5,
  page		= {349--360}
}

###Article{	  man93,
  author	= "Mangasarian, O. L.",
  title		= "Mathematical programming in neural networks",
  journal	= "ORSA J. Comput.",
  year		= 1993,
  volume	= 5,
  page		= {349--360}
}

###Article{	  man93,
  author	= "Mangasarian, O. L.",
  title		= "Mathematical programming in neural networks",
  journal	= "ORSA J. Comput.",
  year		= 1993,
  volume	= 5,
  page		= {349--360}
}

@Article{	  man95,
  author	= "Mangasarian, O. L.",
  title		= "Parallel gradient distribution in unconstrained
		  optimization",
  journal	= siamcontrol,
  year		= 1995,
  volume	= 33,
  page		= {1916--1925}
}

###Article{	  man95,
  author	= "Mangasarian, O. L.",
  title		= "Parallel gradient distribution in unconstrained
		  optimization",
  journal	= siamcontrol,
  year		= 1995,
  volume	= 33,
  page		= {1916--1925}
}

###Article{	  man95,
  author	= "Mangasarian, O. L.",
  title		= "Parallel gradient distribution in unconstrained
		  optimization",
  journal	= siamcontrol,
  year		= 1995,
  volume	= 33,
  page		= {1916--1925}
}

@Book{		  mang:1969,
  address	= {New York},
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= mcgrawhill,
  title		= {Nonlinear Programming},
  year		= 1969
}

###Book{	  mang:1969,
  address	= {New York},
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= mcgrawhill,
  title		= {Nonlinear Programming},
  year		= 1969
}

###Book{	  mang:1969,
  address	= {New York},
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= mcgrawhill,
  title		= {Nonlinear Programming},
  year		= 1969
}

@Article{	  mang:1984,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {206--216},
  title		= {Normal solutions of linear programs},
  volume	= 22,
  year		= 1984
}

###Article{	  mang:1984,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {206--216},
  title		= {Normal solutions of linear programs},
  volume	= 22,
  year		= 1984
}

###Article{	  mang:1984,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {206--216},
  title		= {Normal solutions of linear programs},
  volume	= 22,
  year		= 1984
}

@Article{	  mang:1985,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamcontrol,
  pages		= {30-37},
  title		= {Sufficiency of exact penalty minimization},
  volume	= 23,
  year		= 1985
}

###Article{	  mang:1985,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamcontrol,
  pages		= {30-37},
  title		= {Sufficiency of exact penalty minimization},
  volume	= 23,
  year		= 1985
}

###Article{	  mang:1985,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= siamcontrol,
  pages		= {30-37},
  title		= {Sufficiency of exact penalty minimization},
  volume	= 23,
  year		= 1985
}

@Article{	  mang:1988,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= {Oper. Res. Lett.},
  pages		= {21--26},
  title		= {A simple characterization of solution sets of convex
		  programs},
  volume	= 7,
  year		= 1988
}

###Article{	  mang:1988,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= {Oper. Res. Lett.},
  pages		= {21--26},
  title		= {A simple characterization of solution sets of convex
		  programs},
  volume	= 7,
  year		= 1988
}

###Article{	  mang:1988,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= {Oper. Res. Lett.},
  pages		= {21--26},
  title		= {A simple characterization of solution sets of convex
		  programs},
  volume	= 7,
  year		= 1988
}

@Book{		  mang:1994,
  address	= {New York},
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {Gene Golub},
  note		= {Originally published: New York, McGraw-Hill, 1969.},
  publisher	= siam,
  series	= {Classics in Applied Mathematics},
  title		= {Nonlinear Programming},
  volume	= 10,
  year		= 1994
}

###Book{	  mang:1994,
  address	= {New York},
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {Gene Golub},
  note		= {Originally published: New York, McGraw-Hill, 1969.},
  publisher	= siam,
  series	= {Classics in Applied Mathematics},
  title		= {Nonlinear Programming},
  volume	= 10,
  year		= 1994
}

###Book{	  mang:1994,
  address	= {New York},
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {Gene Golub},
  note		= {Originally published: New York, McGraw-Hill, 1969.},
  publisher	= siam,
  series	= {Classics in Applied Mathematics},
  title		= {Nonlinear Programming},
  volume	= 10,
  year		= 1994
}

@Article{	  mang:2004,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= jota,
  pages		= {1-18},
  title		= {A {N}ewton method for linear programming},
  volume	= 121,
  year		= 2004
}

###Article{	  mang:2004,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= jota,
  pages		= {1-18},
  title		= {A {N}ewton method for linear programming},
  volume	= 121,
  year		= 2004
}

###Article{	  mang:2004,
  author	= {O. L. Mangasarian},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= jota,
  pages		= {1-18},
  title		= {A {N}ewton method for linear programming},
  volume	= 121,
  year		= 2004
}

@Article{	  mangfrom:1967,
  author	= {O. L. Mangasarian and S. Fromovitz},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathanaappl,
  pages		= {37-47},
  title		= {The {F}ritz-{J}ohn conditions in the presence of equality
		  and inequality constraints},
  volume	= 17,
  year		= 1967
}

###Article{	  mangfrom:1967,
  author	= {O. L. Mangasarian and S. Fromovitz},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathanaappl,
  pages		= {37-47},
  title		= {The {F}ritz-{J}ohn conditions in the presence of equality
		  and inequality constraints},
  volume	= 17,
  year		= 1967
}

###Article{	  mangfrom:1967,
  author	= {O. L. Mangasarian and S. Fromovitz},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathanaappl,
  pages		= {37-47},
  title		= {The {F}ritz-{J}ohn conditions in the presence of equality
		  and inequality constraints},
  volume	= 17,
  year		= 1967
}

@Article{	  mangmeye:1979,
  author	= {O. L. Mangasarian and R. R. Meyer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamcontrol,
  month		= {November},
  number	= 6,
  pages		= {745--752},
  title		= {Nonlinear Perturbation of Linear Programs},
  volume	= 17,
  year		= 1979
}

###Article{	  mangmeye:1979,
  author	= {O. L. Mangasarian and R. R. Meyer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamcontrol,
  month		= {November},
  number	= 6,
  pages		= {745--752},
  title		= {Nonlinear Perturbation of Linear Programs},
  volume	= 17,
  year		= 1979
}

###Article{	  mangmeye:1979,
  author	= {O. L. Mangasarian and R. R. Meyer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamcontrol,
  month		= {November},
  number	= 6,
  pages		= {745--752},
  title		= {Nonlinear Perturbation of Linear Programs},
  volume	= 17,
  year		= 1979
}

@TechReport{	  mannrich:2004,
  address	= {CA},
  author	= {Alan S. Manne and Richard G. Richels},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Stanford University},
  month		= {June},
  title		= {{MERGE}: An intergrated assessment model for global
		  climate change},
  year		= 2004
}

###TechReport{	  mannrich:2004,
  address	= {CA},
  author	= {Alan S. Manne and Richard G. Richels},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Stanford University},
  month		= {June},
  title		= {{MERGE}: An intergrated assessment model for global
		  climate change},
  year		= 2004
}

###TechReport{	  mannrich:2004,
  address	= {CA},
  author	= {Alan S. Manne and Richard G. Richels},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Stanford University},
  month		= {June},
  title		= {{MERGE}: An intergrated assessment model for global
		  climate change},
  year		= 2004
}

@PhDThesis{	  mara:1978,
  address	= {London, UK},
  author	= {N. Maratos},
  date-modified	= {2007-07-18 14:59:32 -0700},
  school	= {Imperial College of Science and Technology},
  title		= {Exact penalty function algorithms for finite dimensional
		  and optimization problems},
  year		= 1978
}

###PhDThesis{	  mara:1978,
  address	= {London, UK},
  author	= {N. Maratos},
  date-modified	= {2007-07-18 14:59:32 -0700},
  school	= {Imperial College of Science and Technology},
  title		= {Exact penalty function algorithms for finite dimensional
		  and optimization problems},
  year		= 1978
}

###PhDThesis{	  mara:1978,
  address	= {London, UK},
  author	= {N. Maratos},
  date-modified	= {2007-07-18 14:59:32 -0700},
  school	= {Imperial College of Science and Technology},
  title		= {Exact penalty function algorithms for finite dimensional
		  and optimization problems},
  year		= 1978
}

@Article{	  mark:1956,
  author	= {H. M. Markowitz},
  title		= {The optimization of a quadratic function subject to
		  constraints},
  journal	= {Nav. Res. Logist. Quart.},
  volume	= 3,
  year		= 1956,
  pages		= {111--133}
}

###Article{	  mark:1956,
  author	= {H. M. Markowitz},
  title		= {The optimization of a quadratic function subject to
		  constraints},
  journal	= {Nav. Res. Logist. Quart.},
  volume	= 3,
  year		= 1956,
  pages		= {111--133}
}

###Article{	  mark:1956,
  author	= {H. M. Markowitz},
  title		= {The optimization of a quadratic function subject to
		  constraints},
  journal	= {Nav. Res. Logist. Quart.},
  volume	= 3,
  year		= 1956,
  pages		= {111--133}
}

@Article{	  martinet:1970,
  author	= {Martinet, B.},
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		  approximations successives},
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  volume	= 4,
  number	= {Ser. R-3},
  year		= 1970,
  pages		= {154–158}
}

###Article{	  martinet:1970,
  author	= {Martinet, B.},
  title		= {R\'egularisation d'in\'equations variationnelles par
		  approximations successives},
  journal	= {Rev. Fran¸caise Informat. Recherche Op´erationnelle},
  volume	= 4,
  number	= {Ser. R-3},
  year		= 1970,
  pages		= {154–158}
}

@Manual{	  math:1992,
  address	= {Natick, MA},
  author	= {MathWorks},
  date-modified	= {2007-07-18 14:59:33 -0700},
  organization	= {The {MathWorks}, Inc.},
  title		= {{MATLAB} User's Guide},
  year		= 1992
}

###Manual{	  math:1992,
  address	= {Natick, MA},
  author	= {MathWorks},
  date-modified	= {2007-07-18 14:59:33 -0700},
  organization	= {The {MathWorks}, Inc.},
  title		= {{MATLAB} User's Guide},
  year		= 1992
}

###Manual{	  math:1992,
  address	= {Natick, MA},
  author	= {MathWorks},
  date-modified	= {2007-07-18 14:59:33 -0700},
  organization	= {The {MathWorks}, Inc.},
  title		= {{MATLAB} User's Guide},
  year		= 1992
}

@Manual{	  math:1995,
  address	= {Natick, MA},
  author	= {MathWorks},
  date-modified	= {2007-07-18 14:59:33 -0700},
  organization	= {The {MathWorks}, Inc.},
  title		= {{MATLAB}: External Interfaces},
  year		= 1995
}

###Manual{	  math:1995,
  address	= {Natick, MA},
  author	= {MathWorks},
  date-modified	= {2007-07-18 14:59:33 -0700},
  organization	= {The {MathWorks}, Inc.},
  title		= {{MATLAB}: External Interfaces},
  year		= 1995
}

###Manual{	  math:1995,
  address	= {Natick, MA},
  author	= {MathWorks},
  date-modified	= {2007-07-18 14:59:33 -0700},
  organization	= {The {MathWorks}, Inc.},
  title		= {{MATLAB}: External Interfaces},
  year		= 1995
}

@Article{	  maynpola:1982,
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  title		= {A superlinearly convergent algorithm for constrained
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  volume	= 16,
  year		= 1982
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###Article{	  maynpola:1982,
  author	= {D. Q. Mayne and E. Polak},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprogstudy,
  pages		= {45-61},
  title		= {A superlinearly convergent algorithm for constrained
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  volume	= 16,
  year		= 1982
}

###Article{	  maynpola:1982,
  author	= {D. Q. Mayne and E. Polak},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprogstudy,
  pages		= {45-61},
  title		= {A superlinearly convergent algorithm for constrained
		  optimization problems},
  volume	= 16,
  year		= 1982
}

@Article{	  maz93,
  author	= "Mallat, S. and Zhang, Z.",
  title		= "Matching pursuits with time-frequency dictionaries",
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  page		= {3397--3415}
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###Article{	  maz93,
  author	= "Mallat, S. and Zhang, Z.",
  title		= "Matching pursuits with time-frequency dictionaries",
  journal	= "Signal Processing, IEEE Transactions",
  year		= 1993,
  volume	= 41,
  page		= {3397--3415}
}

###Article{	  maz93,
  author	= "Mallat, S. and Zhang, Z.",
  title		= "Matching pursuits with time-frequency dictionaries",
  journal	= "Signal Processing, IEEE Transactions",
  year		= 1993,
  volume	= 41,
  page		= {3397--3415}
}

@Article{	  mcco:1971,
  author	= {Garth P. McCormick},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {217-238},
  title		= {Penalty Function Versus Non-Penalty Function Methods for
		  Constrained Nonlinear Programming Problems},
  volume	= 1,
  year		= 1971
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###Article{	  mcco:1971,
  author	= {Garth P. McCormick},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {217-238},
  title		= {Penalty Function Versus Non-Penalty Function Methods for
		  Constrained Nonlinear Programming Problems},
  volume	= 1,
  year		= 1971
}

###Article{	  mcco:1971,
  author	= {Garth P. McCormick},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {217-238},
  title		= {Penalty Function Versus Non-Penalty Function Methods for
		  Constrained Nonlinear Programming Problems},
  volume	= 1,
  year		= 1971
}

@Book{		  mclature:1993,
  address	= {Mill Valley, CA},
  author	= {Fred W. McLafferty and Franti{\u{s}}ek Ture{\u{c}}ek},
  publisher	= {University Science Books},
  title		= {Interpretation of Mass Spectra},
  edition	= {Fourth},
  year		= 1993
}

###Book{	  mclature:1993,
  address	= {Mill Valley, CA},
  author	= {Fred W. McLafferty and Franti{\u{s}}ek Ture{\u{c}}ek},
  publisher	= {University Science Books},
  title		= {Interpretation of Mass Spectra},
  edition	= {Fourth},
  year		= 1993
}

###Book{	  mclature:1993,
  address	= {Mill Valley, CA},
  author	= {Fred W. McLafferty and Franti{\u{s}}ek Ture{\u{c}}ek},
  publisher	= {University Science Books},
  title		= {Interpretation of Mass Spectra},
  edition	= {Fourth},
  year		= 1993
}

@Article{	  mehrotra2000volumetric,
  title		= {Volumetric center method for stochastic convex programs
		  using sampling},
  author	= {Mehrotra, Sanjay},
  year		= {2000},
  publisher	= {Humboldt-Universit{\"a}t zu Berlin,
		  Mathematisch-Naturwissenschaftliche Fakult{\"a}t II,
		  Institut f{\"u}r Mathematik}
}

@Article{	  mendpajotomc:2007,
  author	= {S. Mendelson and A. Pajor and N. Tomczak-Jaegermann},
  title		= {Uniform uncertainty principle for {B}ernoulli and
		  subgaussian ensembles},
  journal	= {Constructive Approximation},
  note		= {To appear},
  doi		= {10.1007/s00365-007-9005-8},
  year		= 2008
}

###Article{	  mendpajotomc:2007,
  author	= {S. Mendelson and A. Pajor and N. Tomczak-Jaegermann},
  title		= {Uniform uncertainty principle for {B}ernoulli and
		  subgaussian ensembles},
  journal	= {Constructive Approximation},
  note		= {To appear},
  doi		= {10.1007/s00365-007-9005-8},
  year		= 2008
}

###Article{	  mendpajotomc:2007,
  author	= {S. Mendelson and A. Pajor and N. Tomczak-Jaegermann},
  title		= {Uniform uncertainty principle for {B}ernoulli and
		  subgaussian ensembles},
  journal	= {Constructive Approximation},
  note		= {To appear},
  doi		= {10.1007/s00365-007-9005-8},
  year		= 2008
}

@Article{	  mgb08,
  author	= {L. Meier and S. van de Geer and P. B{\"{u}}hlmann},
  title		= {The Group {L}asso for Logistic Regression},
  journal	= {J. Royal Statist. Soc. B},
  volume	= 70,
  pages		= {53--71},
  year		= 2008
}

###Article{	  mgb08,
  author	= {L. Meier and S. van de Geer and P. B{\"{u}}hlmann},
  title		= {The Group {L}asso for Logistic Regression},
  journal	= {J. Royal Statist. Soc. B},
  volume	= 70,
  pages		= {53--71},
  year		= 2008
}

###Article{	  mgb08,
  author	= {L. Meier and S. van de Geer and P. B{\"{u}}hlmann},
  title		= {The Group {L}asso for Logistic Regression},
  journal	= {J. Royal Statist. Soc. B},
  volume	= 70,
  pages		= {53--71},
  year		= 2008
}

@TechReport{	  mgc08,
  author	= "S. Ma and D. Goldfarb and L. Chen",
  title		= "Fixed point and {Bregman} iterative methods for matrix
		  rank minimization",
  type		= "Report",
  number	= "08-78",
  institution	= "UCLA Computational and Applied Mathematics",
  year		= 2008
}

@Article{	  mgc:2009,
  author	= "S. Ma and D. Goldfarb and L. Chen",
  title		= "Fixed point and {Bregman} iterative methods for matrix
		  rank minimization",
  journal	= mathprog,
  volume	= 128,
  number	= {1-2},
  pages		= {321-353},
  year		= 2009
}

@Article{	  mif81,
  author	= "Mine, H. and Fukushima, M.",
  title		= "A minimization method for the sum of a convex function and
		  a continuously differentiable function",
  journal	= jota,
  year		= 1981,
  volume	= 33,
  page		= {9--23}
}

###Article{	  mif81,
  author	= "Mine, H. and Fukushima, M.",
  title		= "A minimization method for the sum of a convex function and
		  a continuously differentiable function",
  journal	= jota,
  year		= 1981,
  volume	= 33,
  page		= {9--23}
}

###Article{	  mif81,
  author	= "Mine, H. and Fukushima, M.",
  title		= "A minimization method for the sum of a convex function and
		  a continuously differentiable function",
  journal	= jota,
  year		= 1981,
  volume	= 33,
  page		= {9--23}
}

@Article{	  mill1:1970,
  author	= {Keith Miller},
  date-added	= {2007-12-13 11:48:09 -0800},
  date-modified	= {2007-12-13 11:48:09 -0800},
  journal	= siammath,
  number	= 1,
  pages		= {52--74},
  title		= {Least squares methods for ill-posed problems with a
		  prescribed bound},
  volume	= 1,
  year		= 1970
}

###Article{	  mill1:1970,
  author	= {Keith Miller},
  date-added	= {2007-12-13 11:48:09 -0800},
  date-modified	= {2007-12-13 11:48:09 -0800},
  journal	= siammath,
  number	= 1,
  pages		= {52--74},
  title		= {Least squares methods for ill-posed problems with a
		  prescribed bound},
  volume	= 1,
  year		= 1970
}

###Article{	  mill1:1970,
  author	= {Keith Miller},
  date-added	= {2007-12-13 11:48:09 -0800},
  date-modified	= {2007-12-13 11:48:09 -0800},
  journal	= siammath,
  number	= 1,
  pages		= {52--74},
  title		= {Least squares methods for ill-posed problems with a
		  prescribed bound},
  volume	= 1,
  year		= 1970
}

@Article{	  mill:1970,
  author	= {Keith Miller},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siammath,
  number	= 1,
  pages		= {52--74},
  title		= {Least squares methods for ill-posed problems with a
		  prescribed bound},
  volume	= 1,
  year		= 1970
}

###Article{	  mill:1970,
  author	= {Keith Miller},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siammath,
  number	= 1,
  pages		= {52--74},
  title		= {Least squares methods for ill-posed problems with a
		  prescribed bound},
  volume	= 1,
  year		= 1970
}

###Article{	  mill:1970,
  author	= {Keith Miller},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siammath,
  number	= 1,
  pages		= {52--74},
  title		= {Least squares methods for ill-posed problems with a
		  prescribed bound},
  volume	= 1,
  year		= 1970
}

@Misc{		  mishyoni:2008,
  author	= {Moshe Mishali and Yonina C. Eldar},
  title		= {Reduce and Boost: Recovering Arbitrary Sets of Jointly
		  Sparse Vectors},
  url		= {http://arxiv.org/abs/0802.1311},
  howpublished	= {arXiv 0802.1311},
  month		= {February},
  year		= 2008
}

###Misc{	  mishyoni:2008,
  author	= {Moshe Mishali and Yonina C. Eldar},
  title		= {Reduce and Boost: Recovering Arbitrary Sets of Jointly
		  Sparse Vectors},
  url		= {http://arxiv.org/abs/0802.1311},
  howpublished	= {arXiv 0802.1311},
  month		= {February},
  year		= 2008
}

###Misc{	  mishyoni:2008,
  author	= {Moshe Mishali and Yonina C. Eldar},
  title		= {Reduce and Boost: Recovering Arbitrary Sets of Jointly
		  Sparse Vectors},
  url		= {http://arxiv.org/abs/0802.1311},
  howpublished	= {arXiv 0802.1311},
  month		= {February},
  year		= 2008
}

@Misc{		  mnist,
  author	= {Yann LeCun and Corinna Cortes},
  title		= {The {MNIST} database},
  howpublished	= {\url{http://yann.lecun.com/exdb/mnist/}},
  year		= 2011
}

###Misc{	  mnist,
  author	= {Yann LeCun and Corinna Cortes},
  title		= {The {MNIST} database},
  howpublished	= {\url{http://yann.lecun.com/exdb/mnist/}},
  year		= 2011
}

###Misc{	  mnist,
  author	= {Yann LeCun and Corinna Cortes},
  title		= {The {MNIST} database},
  howpublished	= {\url{http://yann.lecun.com/exdb/mnist/}},
  year		= 2011
}

@Article{	  moguprie:2003,
  author	= {J.M. Moguerza and F.J. Prieto},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Math. Program. A},
  pages		= {573-616},
  title		= {An augmented Lagrangian interior-point method using
		  directions of negative curvature},
  volume	= 95,
  year		= 2003
}

###Article{	  moguprie:2003,
  author	= {J.M. Moguerza and F.J. Prieto},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Math. Program. A},
  pages		= {573-616},
  title		= {An augmented Lagrangian interior-point method using
		  directions of negative curvature},
  volume	= 95,
  year		= 2003
}

###Article{	  moguprie:2003,
  author	= {J.M. Moguerza and F.J. Prieto},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Math. Program. A},
  pages		= {573-616},
  title		= {An augmented Lagrangian interior-point method using
		  directions of negative curvature},
  volume	= 95,
  year		= 2003
}

@Article{	  moguprie:2003b,
  author	= {J. M. Moguerza and F. J. Prieto},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Math. Program. A},
  pages		= {529-559},
  title		= {Combining search directions using gradient flows},
  volume	= 96,
  year		= 2003
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###Article{	  moguprie:2003b,
  author	= {J. M. Moguerza and F. J. Prieto},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Math. Program. A},
  pages		= {529-559},
  title		= {Combining search directions using gradient flows},
  volume	= 96,
  year		= 2003
}

###Article{	  moguprie:2003b,
  author	= {J. M. Moguerza and F. J. Prieto},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Math. Program. A},
  pages		= {529-559},
  title		= {Combining search directions using gradient flows},
  volume	= 96,
  year		= 2003
}

@InCollection{	  more:1983,
  address	= {Berlin},
  author	= {J. J. Mor\'{e}},
  booktitle	= {Mathematical Programming : The State of the Art},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {A. Bachem and M. Groetschel and B. Korte},
  pages		= {258-287},
  publisher	= springer,
  title		= {Recent developments in algorithms and software for trust
		  region methods},
  year		= 1983
}

###InCollection{  more:1983,
  address	= {Berlin},
  author	= {J. J. Mor\'{e}},
  booktitle	= {Mathematical Programming : The State of the Art},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {A. Bachem and M. Groetschel and B. Korte},
  pages		= {258-287},
  publisher	= springer,
  title		= {Recent developments in algorithms and software for trust
		  region methods},
  year		= 1983
}

###InCollection{  more:1983,
  address	= {Berlin},
  author	= {J. J. Mor\'{e}},
  booktitle	= {Mathematical Programming : The State of the Art},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {A. Bachem and M. Groetschel and B. Korte},
  pages		= {258-287},
  publisher	= springer,
  title		= {Recent developments in algorithms and software for trust
		  region methods},
  year		= 1983
}

@Article{	  moremuns:2004,
  author	= {Jorge J. Mor\'{e} and Todd S. Munson},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathprog,
  pages		= {151--182},
  title		= {Computing mountain passes and transition states},
  volume	= 100,
  year		= 2004
}

###Article{	  moremuns:2004,
  author	= {Jorge J. Mor\'{e} and Todd S. Munson},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathprog,
  pages		= {151--182},
  title		= {Computing mountain passes and transition states},
  volume	= 100,
  year		= 2004
}

###Article{	  moremuns:2004,
  author	= {Jorge J. Mor\'{e} and Todd S. Munson},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathprog,
  pages		= {151--182},
  title		= {Computing mountain passes and transition states},
  volume	= 100,
  year		= 2004
}

@Article{	  moresorensen:1983,
  author	= {Jorge J. More and D. C. Sorensen},
  title		= {Computing a Trust Region Step},
  publisher	= {SIAM},
  year		= 1983,
  journal	= {SIAM Journal on Scientific and Statistical Computing},
  volume	= 4,
  number	= 3,
  pages		= {553-572},
  keywords	= {Newton's method; trust region; ellipsoidal constraint;
		  global convergence},
  url		= {http://link.aip.org/link/?SCE/4/553/1},
  doi		= {10.1137/0904038}
}

###Article{	  moresorensen:1983,
  author	= {Jorge J. More and D. C. Sorensen},
  title		= {Computing a Trust Region Step},
  publisher	= {SIAM},
  year		= 1983,
  journal	= {SIAM Journal on Scientific and Statistical Computing},
  volume	= 4,
  number	= 3,
  pages		= {553-572},
  keywords	= {{Newton's} method; trust region; ellipsoidal constraint;
		  global convergence},
  url		= {http://link.aip.org/link/?SCE/4/553/1},
  doi		= {10.1137/0904038}
}

###Article{	  moresorensen:1983,
  author	= {Jorge J. More and D. C. Sorensen},
  title		= {Computing a Trust Region Step},
  publisher	= {SIAM},
  year		= 1983,
  journal	= {SIAM Journal on Scientific and Statistical Computing},
  volume	= 4,
  number	= 3,
  pages		= {553-572},
  keywords	= {Newton's method; trust region; ellipsoidal constraint;
		  global convergence},
  url		= {http://link.aip.org/link/?SCE/4/553/1},
  doi		= {10.1137/0904038}
}

@Article{	  moretora:1989,
  abstract	= {Presents an algorithm which combines standard active set
		  strategies with the gradient projection method for the
		  solution of quadratic programming problems subject to
		  bounds. The authors show, in particular, that if the
		  quadratic is bounded below on the feasible set then
		  termination occurs at a stationary point in a finite number
		  of iterations. Moreover, if all stationary points are
		  nondegenerate, termination occurs at a local minimizer. A
		  numerical comparison of the algorithm based on the gradient
		  projection algorithm with a standard active set strategy
		  shows that on mildly degenerate problems the gradient
		  projection algorithm requires considerable less iterations
		  and time than the active set strategy. On nondegenerate
		  problems the number of iterations typically decreases by at
		  least a factor of 10. For strongly degenerate problems, the
		  performance of the gradient projection algorithm
		  deteriorates, but it still performs better than the active
		  set method.},
  author	= {J. J. Mor\'{e} and G. Toraldo},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Numerische Mathematik},
  number	= 4,
  pages		= {377--400},
  title		= {Algorithms for bound constrained quadratic programming
		  problems},
  volume	= 55,
  year		= 1989
}

###Article{	  moretora:1989,
  abstract	= {Presents an algorithm which combines standard active set
		  strategies with the gradient projection method for the
		  solution of quadratic programming problems subject to
		  bounds. The authors show, in particular, that if the
		  quadratic is bounded below on the feasible set then
		  termination occurs at a stationary point in a finite number
		  of iterations. Moreover, if all stationary points are
		  nondegenerate, termination occurs at a local minimizer. A
		  numerical comparison of the algorithm based on the gradient
		  projection algorithm with a standard active set strategy
		  shows that on mildly degenerate problems the gradient
		  projection algorithm requires considerable less iterations
		  and time than the active set strategy. On nondegenerate
		  problems the number of iterations typically decreases by at
		  least a factor of 10. For strongly degenerate problems, the
		  performance of the gradient projection algorithm
		  deteriorates, but it still performs better than the active
		  set method.},
  author	= {J. J. Mor\'{e} and G. Toraldo},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Numerische Mathematik},
  number	= 4,
  pages		= {377--400},
  title		= {Algorithms for bound constrained quadratic programming
		  problems},
  volume	= 55,
  year		= 1989
}

###Article{	  moretora:1989,
  abstract	= {Presents an algorithm which combines standard active set
		  strategies with the gradient projection method for the
		  solution of quadratic programming problems subject to
		  bounds. The authors show, in particular, that if the
		  quadratic is bounded below on the feasible set then
		  termination occurs at a stationary point in a finite number
		  of iterations. Moreover, if all stationary points are
		  nondegenerate, termination occurs at a local minimizer. A
		  numerical comparison of the algorithm based on the gradient
		  projection algorithm with a standard active set strategy
		  shows that on mildly degenerate problems the gradient
		  projection algorithm requires considerable less iterations
		  and time than the active set strategy. On nondegenerate
		  problems the number of iterations typically decreases by at
		  least a factor of 10. For strongly degenerate problems, the
		  performance of the gradient projection algorithm
		  deteriorates, but it still performs better than the active
		  set method.},
  author	= {J. J. Mor\'{e} and G. Toraldo},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= {Numerische Mathematik},
  number	= 4,
  pages		= {377--400},
  title		= {Algorithms for bound constrained quadratic programming
		  problems},
  volume	= 55,
  year		= 1989
}

@Article{	  moretora:1991,
  author	= {J. J. Mor\'{e} and G. Toraldo},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {93--113},
  title		= {On the Solution of Large Quadratic Programming Problems
		  with Bound Constraints},
  volume	= 1,
  year		= 1991
}

###Article{	  moretora:1991,
  author	= {J. J. Mor\'{e} and G. Toraldo},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {93--113},
  title		= {On the Solution of Large Quadratic Programming Problems
		  with Bound Constraints},
  volume	= 1,
  year		= 1991
}

###Article{	  moretora:1991,
  author	= {J. J. Mor\'{e} and G. Toraldo},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {93--113},
  title		= {On the Solution of Large Quadratic Programming Problems
		  with Bound Constraints},
  volume	= 1,
  year		= 1991
}

@Misc{		  mosek,
  date-added	= {2007-12-13 11:47:47 -0800},
  date-modified	= {2007-12-13 11:47:47 -0800},
  howpublished	= {Mathematical programming system,
		  \url{http://www.mosek.com}},
  key		= {MOSEK},
  year		= 2007
}

###Misc{	  mosek,
  date-added	= {2007-12-13 11:47:47 -0800},
  date-modified	= {2007-12-13 11:47:47 -0800},
  howpublished	= {Mathematical programming system,
		  \url{http://www.mosek.com}},
  key		= {MOSEK},
  year		= 2007
}

###Misc{	  mosek,
  date-added	= {2007-12-13 11:47:47 -0800},
  date-modified	= {2007-12-13 11:47:47 -0800},
  howpublished	= {Mathematical programming system,
		  \url{http://www.mosek.com}},
  key		= {MOSEK},
  year		= 2007
}

@Article{	  mow97,
  author	= "More, J. J. and Wu, Z.",
  title		= "Global continuation for distance geometry problems",
  journal	= siamopt,
  year		= 1997,
  volume	= 7,
  page		= {814--836}
}

###Article{	  mow97,
  author	= "More, J. J. and Wu, Z.",
  title		= "Global continuation for distance geometry problems",
  journal	= siamopt,
  year		= 1997,
  volume	= 7,
  page		= {814--836}
}

###Article{	  mow97,
  author	= "More, J. J. and Wu, Z.",
  title		= "Global continuation for distance geometry problems",
  journal	= siamopt,
  year		= 1997,
  volume	= 7,
  page		= {814--836}
}

@Article{	  mr1052832,
  author	= {Wright, S. J.},
  coden		= {JOTABN},
  date-added	= {2007-10-20 15:30:07 -0700},
  date-modified	= {2007-10-20 15:30:07 -0700},
  fjournal	= {Journal of Optimization Theory and Applications},
  issn		= {0022-3239},
  journal	= jota,
  mrclass	= {90C05 (90C06 90C20)},
  mrnumber	= {MR1052832 (91d:90062)},
  mrreviewer	= {Christian Michelot},
  number	= 3,
  pages		= {531--554},
  title		= {Implementing proximal point methods for linear
		  programming},
  volume	= 65,
  year		= 1990
}

###Article{	  mr1052832,
  author	= {Wright, S. J.},
  coden		= {JOTABN},
  date-added	= {2007-10-20 15:30:07 -0700},
  date-modified	= {2007-10-20 15:30:07 -0700},
  fjournal	= {Journal of Optimization Theory and Applications},
  journal	= jota,
  mrclass	= {90C05 (90C06 90C20)},
  mrnumber	= {MR1052832 (91d:90062)},
  mrreviewer	= {Christian Michelot},
  number	= 3,
  pages		= {531--554},
  title		= {Implementing proximal point methods for linear
		  programming},
  volume	= 65,
  year		= 1990
}

###Article{	  mr1052832,
  author	= {Wright, S. J.},
  coden		= {JOTABN},
  date-added	= {2007-10-20 15:30:07 -0700},
  date-modified	= {2007-10-20 15:30:07 -0700},
  fjournal	= {Journal of Optimization Theory and Applications},
  journal	= jota,
  mrclass	= {90C05 (90C06 90C20)},
  mrnumber	= {MR1052832 (91d:90062)},
  mrreviewer	= {Christian Michelot},
  number	= 3,
  pages		= {531--554},
  title		= {Implementing proximal point methods for linear
		  programming},
  volume	= 65,
  year		= 1990
}

@InCollection{	  murr:1969,
  address	= {London and New York},
  author	= {W. Murray},
  booktitle	= {Optimization},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {R. Fletcher},
  publisher	= academic,
  title		= {An algorithm for constrained minimization},
  year		= 1969
}

###InCollection{  murr:1969,
  address	= {London and New York},
  author	= {W. Murray},
  booktitle	= {Optimization},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {R. Fletcher},
  publisher	= academic,
  title		= {An algorithm for constrained minimization},
  year		= 1969
}

###InCollection{  murr:1969,
  address	= {London and New York},
  author	= {W. Murray},
  booktitle	= {Optimization},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {R. Fletcher},
  publisher	= academic,
  title		= {An algorithm for constrained minimization},
  year		= 1969
}

@Article{	  murr:1971,
  author	= {W. Murray},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= jota,
  pages		= {189--196},
  title		= {Analytical expressions for the eigenvalues and
		  eigenvectors of the Hessian matrices of barrier and penalty
		  functions},
  volume	= 7,
  year		= 1971
}

###Article{	  murr:1971,
  author	= {W. Murray},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= jota,
  pages		= {189--196},
  title		= {Analytical expressions for the eigenvalues and
		  eigenvectors of the Hessian matrices of barrier and penalty
		  functions},
  volume	= 7,
  year		= 1971
}

###Article{	  murr:1971,
  author	= {W. Murray},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= jota,
  pages		= {189--196},
  title		= {Analytical expressions for the eigenvalues and
		  eigenvectors of the Hessian matrices of barrier and penalty
		  functions},
  volume	= 7,
  year		= 1971
}

@Article{	  murrprie:1995,
  author	= {W. Murray and F. J. Prieto},
  date-modified	= {2007-10-13 17:59:03 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {590--640},
  title		= {A sequential quadratic programming algorithm using an
		  incomplete solution of the subproblem},
  volume	= 5,
  year		= 1995
}

###Article{	  murrprie:1995,
  author	= {W. Murray and F. J. Prieto},
  date-modified	= {2007-10-13 17:59:03 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {590--640},
  title		= {A sequential quadratic programming algorithm using an
		  incomplete solution of the subproblem},
  volume	= 5,
  year		= 1995
}

###Article{	  murrprie:1995,
  author	= {W. Murray and F. J. Prieto},
  date-modified	= {2007-10-13 17:59:03 -0700},
  journal	= siamopt,
  month		= {August},
  number	= 3,
  pages		= {590--640},
  title		= {A sequential quadratic programming algorithm using an
		  incomplete solution of the subproblem},
  volume	= 5,
  year		= 1995
}

@Article{	  murtsaun:1978,
  author	= {B. A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {41-72},
  title		= {Large-scale Linearly Constrained Optimization},
  volume	= 14,
  year		= 1978
}

###Article{	  murtsaun:1978,
  author	= {B. A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {41-72},
  title		= {Large-scale Linearly Constrained Optimization},
  volume	= 14,
  year		= 1978
}

###Article{	  murtsaun:1978,
  author	= {B. A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {41-72},
  title		= {Large-scale Linearly Constrained Optimization},
  volume	= 14,
  year		= 1978
}

@Article{	  murtsaun:1982,
  author	= {Bruce A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprogstudy,
  pages		= {84-117},
  title		= {A Projected {L}agrangian Algorithm and its Implementation
		  for Sparse Nonlinear Constraints},
  volume	= 16,
  year		= 1982
}

###Article{	  murtsaun:1982,
  author	= {Bruce A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprogstudy,
  pages		= {84-117},
  title		= {A Projected {L}agrangian Algorithm and its Implementation
		  for Sparse Nonlinear Constraints},
  volume	= 16,
  year		= 1982
}

###Article{	  murtsaun:1982,
  author	= {Bruce A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprogstudy,
  pages		= {84-117},
  title		= {A Projected {L}agrangian Algorithm and its Implementation
		  for Sparse Nonlinear Constraints},
  volume	= 16,
  year		= 1982
}

@TechReport{	  murtsaun:1983,
  address	= {Department of Management Science and Engineering, Stanford
		  University, Stanford, CA},
  author	= {Bruce A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Systems Optimization Laboratory},
  month		= {December},
  number	= {83-20R},
  title		= {{MINOS 5.5} User's Guide},
  year		= 1983
}

###TechReport{	  murtsaun:1983,
  address	= {Department of Management Science and Engineering, Stanford
		  University, Stanford, CA},
  author	= {Bruce A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Systems Optimization Laboratory},
  month		= {December},
  number	= {83-20R},
  title		= {{MINOS 5.5} User's Guide},
  year		= 1983
}

###TechReport{	  murtsaun:1983,
  address	= {Department of Management Science and Engineering, Stanford
		  University, Stanford, CA},
  author	= {Bruce A. Murtagh and M. A. Saunders},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Systems Optimization Laboratory},
  month		= {December},
  number	= {83-20R},
  title		= {{MINOS 5.5} User's Guide},
  year		= 1983
}

@Manual{	  nag:1977,
  address	= {Wilkinson House, Jordan Hill Road, Oxford, England},
  annote	= {Authors: P. E. Gill, W. Murray and S. M. Picken},
  date-modified	= {2007-12-14 11:58:59 -0800},
  edition	= {Mark 6},
  note		= {{E04UAF}, {E04VAF}, {E04VBF}, {E04WAF}, constrained
		  optimization using sequential augmented {Lagrangian}
		  methods},
  organization	= {Numerical Algorithms Group Limited},
  title		= {The {NAG} {Fortran Library Manual}},
  year		= 1977
}

###Manual{	  nag:1977,
  address	= {Wilkinson House, Jordan Hill Road, Oxford, England},
  annote	= {Authors: P. E. Gill, W. Murray and S. M. Picken},
  date-modified	= {2007-12-14 11:58:59 -0800},
  edition	= {Mark 6},
  note		= {{E04UAF}, {E04VAF}, {E04VBF}, {E04WAF}, constrained
		  optimization using sequential augmented {Lagrangian}
		  methods},
  organization	= {Numerical Algorithms Group Limited},
  title		= {The {NAG} {Fortran Library Manual}},
  year		= 1977
}

###Manual{	  nag:1977,
  address	= {Wilkinson House, Jordan Hill Road, Oxford, England},
  annote	= {Authors: P. E. Gill, W. Murray and S. M. Picken},
  date-modified	= {2007-12-14 11:58:59 -0800},
  edition	= {Mark 6},
  note		= {{E04UAF}, {E04VAF}, {E04VBF}, {E04WAF}, constrained
		  optimization using sequential augmented {Lagrangian}
		  methods},
  organization	= {Numerical Algorithms Group Limited},
  title		= {The {NAG} {Fortran Library Manual}},
  year		= 1977
}

@Book{		  nashsofe:1996,
  address	= {New York},
  author	= {Stephen G. Nash and Ariela Sofer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= mcgrawhill,
  title		= {Linear and Nonlinear Programming},
  year		= 1996
}

###Book{	  nashsofe:1996,
  address	= {New York},
  author	= {Stephen G. Nash and Ariela Sofer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= mcgrawhill,
  title		= {Linear and Nonlinear Programming},
  year		= 1996
}

###Book{	  nashsofe:1996,
  address	= {New York},
  author	= {Stephen G. Nash and Ariela Sofer},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= mcgrawhill,
  title		= {Linear and Nonlinear Programming},
  year		= 1996
}

@Article{	  nata:1995,
  author	= {B. K. Natarajan},
  title		= {Sparse Approximate Solutions to Linear Systems},
  journal	= siamcomp,
  volume	= 24,
  number	= 2,
  year		= 1995,
  month		= {April},
  pages		= {227--234},
  keywords	= {Sparse solutions, linear systems},
  summary	= {The suggested algorithm takes as input $A$, $b$, and
		  $\epsilon$. The algorithm first normalizes each column
		  $a_i$ of $A$. Then, at each iteration of the selection
		  phase, the algorithm greedily picks that column of $A$ that
		  is closest in angle to the vector $b$. Then, $b$ and the
		  column vectors of $A$ are projected onto the subspace
		  orthogonal to the chosen column. This procedure is repeated
		  until $\Vert b\Vert_2 \leq \epsilon$. In the solution
		  phase, the algorithm solves the linear system problem $Bx =
		  b^{(0)} - b^{(r)}$ where $B$ is the matrix consisting of
		  those columns of $A$ that were chosen in the selection
		  phase, $b^{(0)}$ is $b$ at the start of the selection
		  phase, and $b^{(r)}$ is $b$ at the end of the selection phase.},
  abstract	= {The following problem is considered: given a matrix $A$ in
		  $\mathbb{R}^{m\times n}$, ($m$ rows and $n$ columns), a
		  vector $b$ in $\mathbb{R}^m$, and $\epsilon > 0$, compute a
		  vector $x$ satisfying $\Vert Ax-b\Vert_2 \leq \epsilon$ if
		  such exists, such that $x$ has the fewest number of
		  non-zero entries over all such vectors. It is shown that
		  the problem is NP-hard, but that the well-known greedy
		  heuristic is good in that it computes a solution with at
		  most $\lceil 18\mathrm{Opt}(\epsilon/2)
		  \Vert\mathbf{A}^+\Vert_2^2 \ln(\Vert
		  b\Vert_2/\epsilon)\rceil$ non zero entries, where
		  $\mathrm{Opt}(\epsilon/2)$ is the optimum number of nonzero
		  entries at error $\epsilon/2$. $\mathbf{A}$ is the matrix
		  obtained by normalizing each column of $A$ with respect to
		  the $L_2$ norm, and $\mathbf{A}^+$ is its peudo-inverse}
}

###Article{	  nata:1995,
  author	= {B. K. Natarajan},
  title		= {Sparse Approximate Solutions to Linear Systems},
  journal	= siamcomp,
  volume	= 24,
  number	= 2,
  year		= 1995,
  month		= {April},
  pages		= {227--234},
  keywords	= {Sparse solutions, linear systems},
  summary	= {The suggested algorithm takes as input $A$, $b$, and
		  $\epsilon$. The algorithm first normalizes each column
		  $a_i$ of $A$. Then, at each iteration of the selection
		  phase, the algorithm greedily picks that column of $A$ that
		  is closest in angle to the vector $b$. Then, $b$ and the
		  column vectors of $A$ are projected onto the subspace
		  orthogonal to the chosen column. This procedure is repeated
		  until $\Vert b\Vert_2 \leq \epsilon$. In the solution
		  phase, the algorithm solves the linear system problem $Bx =
		  b^{(0)} - b^{(r)}$ where $B$ is the matrix consisting of
		  those columns of $A$ that were chosen in the selection
		  phase, $b^{(0)}$ is $b$ at the start of the selection
		  phase, and $b^{(r)}$ is $b$ at the end of the selection phase.},
  abstract	= {The following problem is considered: given a matrix $A$ in
		  $\mathbb{R}^{m\times n}$, ($m$ rows and $n$ columns), a
		  vector $b$ in $\mathbb{R}^m$, and $\epsilon > 0$, compute a
		  vector $x$ satisfying $\Vert Ax-b\Vert_2 \leq \epsilon$ if
		  such exists, such that $x$ has the fewest number of
		  non-zero entries over all such vectors. It is shown that
		  the problem is NP-hard, but that the well-known greedy
		  heuristic is good in that it computes a solution with at
		  most $\lceil 18\mathrm{Opt}(\epsilon/2)
		  \Vert\mathbf{A}^+\Vert_2^2 \ln(\Vert
		  b\Vert_2/\epsilon)\rceil$ non zero entries, where
		  $\mathrm{Opt}(\epsilon/2)$ is the optimum number of nonzero
		  entries at error $\epsilon/2$. $\mathbf{A}$ is the matrix
		  obtained by normalizing each column of $A$ with respect to
		  the $L_2$ norm, and $\mathbf{A}^+$ is its peudo-inverse}
}

###Article{	  nata:1995,
  author	= {B. K. Natarajan},
  title		= {Sparse Approximate Solutions to Linear Systems},
  journal	= siamcomp,
  volume	= 24,
  number	= 2,
  year		= 1995,
  month		= {April},
  pages		= {227--234},
  keywords	= {Sparse solutions, linear systems},
  summary	= {The suggested algorithm takes as input $A$, $b$, and
		  $\epsilon$. The algorithm first normalizes each column
		  $a_i$ of $A$. Then, at each iteration of the selection
		  phase, the algorithm greedily picks that column of $A$ that
		  is closest in angle to the vector $b$. Then, $b$ and the
		  column vectors of $A$ are projected onto the subspace
		  orthogonal to the chosen column. This procedure is repeated
		  until $\Vert b\Vert_2 \leq \epsilon$. In the solution
		  phase, the algorithm solves the linear system problem $Bx =
		  b^{(0)} - b^{(r)}$ where $B$ is the matrix consisting of
		  those columns of $A$ that were chosen in the selection
		  phase, $b^{(0)}$ is $b$ at the start of the selection
		  phase, and $b^{(r)}$ is $b$ at the end of the selection phase.},
  abstract	= {The following problem is considered: given a matrix $A$ in
		  $\mathbb{R}^{m\times n}$, ($m$ rows and $n$ columns), a
		  vector $b$ in $\mathbb{R}^m$, and $\epsilon > 0$, compute a
		  vector $x$ satisfying $\Vert Ax-b\Vert_2 \leq \epsilon$ if
		  such exists, such that $x$ has the fewest number of
		  non-zero entries over all such vectors. It is shown that
		  the problem is NP-hard, but that the well-known greedy
		  heuristic is good in that it computes a solution with at
		  most $\lceil 18\mathrm{Opt}(\epsilon/2)
		  \Vert\mathbf{A}^+\Vert_2^2 \ln(\Vert
		  b\Vert_2/\epsilon)\rceil$ non zero entries, where
		  $\mathrm{Opt}(\epsilon/2)$ is the optimum number of nonzero
		  entries at error $\epsilon/2$. $\mathbf{A}$ is the matrix
		  obtained by normalizing each column of $A$ with respect to
		  the $L_2$ norm, and $\mathbf{A}^+$ is its peudo-inverse}
}

@Article{	  neb01,
  author	= "Nedi\'c, A. and Bertsekas, D. P.",
  title		= "Incremental subgradient methods for nondifferentiable
		  optimization",
  journal	= siamopt,
  year		= 2001,
  volume	= 12,
  page		= {109--138}
}

###Article{	  neb01,
  author	= "Nedi\'c, A. and Bertsekas, D. P.",
  title		= "Incremental subgradient methods for nondifferentiable
		  optimization",
  journal	= siamopt,
  year		= 2001,
  volume	= 12,
  page		= {109--138}
}

###Article{	  neb01,
  author	= "Nedi\'c, A. and Bertsekas, D. P.",
  title		= "Incremental subgradient methods for nondifferentiable
		  optimization",
  journal	= siamopt,
  year		= 2001,
  volume	= 12,
  page		= {109--138}
}

@Article{	  nedber:2000,
  title		= {{Convergence rate of incremental subgradient algorithms}},
  author	= {Nedic, A. and Bertsekas, D.},
  journal	= {Stochastic Optimization: Algorithms and Applications},
  pages		= {263--304},
  year		= 2000,
  publisher	= {Kluwer Academic Pub}
}

###Article{	  nedber:2000,
  title		= {{Convergence rate of incremental subgradient algorithms}},
  author	= {Nedic, A. and Bertsekas, D.},
  journal	= {Stochastic Optimization: Algorithms and Applications},
  pages		= {263--304},
  year		= 2000,
  publisher	= {Kluwer Academic Pub}
}

###Article{	  nedber:2000,
  title		= {{Convergence rate of incremental subgradient algorithms}},
  author	= {Nedic, A. and Bertsekas, D.},
  journal	= {Stochastic Optimization: Algorithms and Applications},
  pages		= {263--304},
  year		= 2000,
  publisher	= {Kluwer Academic Pub}
}

@Unpublished{	  needtrop:2008,
  title		= {{CoSaMP}: Iterative signal recovery from incomplete and
		  inaccurate samples},
  author	= {D. Needell and J. A. Tropp},
  month		= {June},
  year		= 2008,
  note		= {To appear in {\it Appl. Comp. Harmonic Anal.}}
}

###Unpublished{	  needtrop:2008,
  title		= {{CoSaMP}: Iterative signal recovery from incomplete and
		  inaccurate samples},
  author	= {D. Needell and J. A. Tropp},
  month		= {June},
  year		= 2008,
  note		= {To appear in {\it Appl. Comp. Harmonic Anal.}}
}

###Unpublished{	  needtrop:2008,
  title		= {{CoSaMP}: Iterative signal recovery from incomplete and
		  inaccurate samples},
  author	= {D. Needell and J. A. Tropp},
  month		= {June},
  year		= 2008,
  note		= {To appear in {\it Appl. Comp. Harmonic Anal.}}
}

@InProceedings{	  needvers:2008,
  author	= {{Needell}, D. and {Vershynin}, R.},
  title		= "{Greedy signal recovery and uncertainty principles}",
  booktitle	= {Society of Photo-Optical Instrumentation Engineers (SPIE)
		  Conference Series},
  year		= 2008,
  series	= {Society of Photo-Optical Instrumentation Engineers (SPIE)
		  Conference Series},
  volume	= 6814,
  month		= mar,
  doi		= {10.1117/12.776996},
  adsurl	= {http://adsabs.harvard.edu/abs/2008SPIE.6814E..13N},
  adsnote	= {Provided by the SAO/NASA Astrophysics Data System}
}

###InProceedings{ needvers:2008,
  author	= {{Needell}, D. and {Vershynin}, R.},
  title		= "{Greedy signal recovery and uncertainty principles}",
  booktitle	= {Society of Photo-Optical Instrumentation Engineers (SPIE)
		  Conference Series},
  year		= 2008,
  series	= {Society of Photo-Optical Instrumentation Engineers (SPIE)
		  Conference Series},
  volume	= 6814,
  month		= mar,
  doi		= {10.1117/12.776996},
  adsurl	= {http://adsabs.harvard.edu/abs/2008SPIE.6814E..13N},
  adsnote	= {Provided by the SAO/NASA Astrophysics Data System}
}

###InProceedings{ needvers:2008,
  author	= {{Needell}, D. and {Vershynin}, R.},
  title		= "{Greedy signal recovery and uncertainty principles}",
  booktitle	= {Society of Photo-Optical Instrumentation Engineers (SPIE)
		  Conference Series},
  year		= 2008,
  series	= {Society of Photo-Optical Instrumentation Engineers (SPIE)
		  Conference Series},
  volume	= 6814,
  month		= mar,
  doi		= {10.1117/12.776996},
  adsurl	= {http://adsabs.harvard.edu/abs/2008SPIE.6814E..13N},
  adsnote	= {Provided by the SAO/NASA Astrophysics Data System}
}

@Article{	  nem05,
  author	= "Nemirovski, A.",
  title		= "Prox-Method with Rate of Convergence $O(1/t)$ for
		  Variational Inequalities with Lipschitz Continuous Monotone
		  Operators and Smooth Convex-Concave Saddle Point Problems",
  journal	= siamopt,
  year		= 2005,
  volume	= 15,
  page		= {229--251}
}

###Article{	  nem05,
  author	= "Nemirovski, A.",
  title		= "Prox-Method with Rate of Convergence $O(1/t)$ for
		  Variational Inequalities with Lipschitz Continuous Monotone
		  Operators and Smooth Convex-Concave Saddle Point Problems",
  journal	= siamopt,
  year		= 2005,
  volume	= 15,
  page		= {229--251}
}

###Article{	  nem05,
  author	= "Nemirovski, A.",
  title		= "Prox-Method with Rate of Convergence $O(1/t)$ for
		  Variational Inequalities with Lipschitz Continuous Monotone
		  Operators and Smooth Convex-Concave Saddle Point Problems",
  journal	= siamopt,
  year		= 2005,
  volume	= 15,
  page		= {229--251}
}

@Article{	  nemirovski1994efficient,
  title		= {{Efficient methods in convex programming}},
  author	= {Nemirovski, A.},
  journal	= {Lecture notes},
  year		= 1994
}

###Article{	  nemirovski1994efficient,
  title		= {{Efficient methods in convex programming}},
  author	= {Nemirovski, A.},
  journal	= {Lecture notes},
  year		= 1994
}

###Article{	  nemirovski1994efficient,
  title		= {{Efficient methods in convex programming}},
  author	= {Nemirovski, A.},
  journal	= {Lecture notes},
  year		= 1994
}

@Article{	  nemirovski2005efficient,
  title		= {Efficient methods in convex programming},
  author	= {Nemirovski, Arkadi},
  year		= {2005}
}

@Article{	  nemirovski2009robust,
  title		= {Robust stochastic approximation approach to stochastic
		  programming},
  author	= {Nemirovski, A. and Juditsky, A. and Lan, G. and Shapiro,
		  A.},
  journal	= siamopt,
  volume	= 19,
  number	= 4,
  pages		= {1574--1609},
  year		= 2009,
  publisher	= {Society for Industrial and Applied Mathematics}
}

###Article{	  nemirovski2009robust,
  title		= {Robust stochastic approximation approach to stochastic
		  programming},
  author	= {Nemirovski, A. and Juditsky, A. and Lan, G. and Shapiro,
		  A.},
  journal	= siamopt,
  volume	= 19,
  number	= 4,
  pages		= {1574--1609},
  year		= 2009,
  publisher	= {Society for Industrial and Applied Mathematics}
}

###Article{	  nemirovski2009robust,
  title		= {Robust stochastic approximation approach to stochastic
		  programming},
  author	= {Nemirovski, A. and Juditsky, A. and Lan, G. and Shapiro,
		  A.},
  journal	= siamopt,
  volume	= 19,
  number	= 4,
  pages		= {1574--1609},
  year		= 2009,
  publisher	= {Society for Industrial and Applied Mathematics}
}

@Book{		  nen94,
  address	= {Philadelphia},
  author	= {Y. E. Nesterov and A. Nemirovski},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= siampub,
  series	= {Stud. Appl. Math.},
  title		= {Interior-Point Polynomial Algorithms in Convex
		  Programming},
  volume	= 13,
  year		= 1994
}

###Book{	  nen94,
  address	= {Philadelphia},
  author	= {Y. E. Nesterov and A. Nemirovski},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= siampub,
  series	= {Stud. Appl. Math.},
  title		= {Interior-Point Polynomial Algorithms in Convex
		  Programming},
  volume	= 13,
  year		= 1994
}

###Book{	  nen94,
  address	= {Philadelphia},
  author	= {Y. E. Nesterov and A. Nemirovski},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= siampub,
  series	= {Stud. Appl. Math.},
  title		= {Interior-Point Polynomial Algorithms in Convex
		  Programming},
  volume	= 13,
  year		= 1994
}

@TechReport{	  nes04a,
  author	= "Nesterov, Y.",
  title		= "Smoothing technique and its applications in semidefinite
		  optimization",
  institution	= "CORE, Catholic University of Louvain, Louvain-la-Neuve",
  address	= "Belgium",
  year		= 2004,
  month		= "October"
}

###TechReport{	  nes04a,
  author	= "Nesterov, Y.",
  title		= "Smoothing technique and its applications in semidefinite
		  optimization",
  institution	= "CORE, Catholic University of Louvain, Louvain-la-Neuve",
  address	= "Belgium",
  year		= 2004,
  month		= "October"
}

###TechReport{	  nes04a,
  author	= "Nesterov, Y.",
  title		= "Smoothing technique and its applications in semidefinite
		  optimization",
  institution	= "CORE, Catholic University of Louvain, Louvain-la-Neuve",
  address	= "Belgium",
  year		= 2004,
  month		= "October"
}

@Book{		  nes04b,
  author	= "Nesterov, Y.",
  title		= "Introductory Lectures on Convex Optimization",
  publisher	= "Kluwer Academic",
  year		= 2004,
  address	= "Dordrecht, The Netherlands"
}

###Book{	  nes04b,
  author	= "Nesterov, Y.",
  title		= "Introductory Lectures on Convex Optimization",
  publisher	= "Kluwer Academic",
  year		= 2004,
  address	= "Dordrecht, The Netherlands"
}

###Book{	  nes04b,
  author	= "Nesterov, Y.",
  title		= "Introductory Lectures on Convex Optimization",
  publisher	= "Kluwer Academic",
  year		= 2004,
  address	= "Dordrecht, The Netherlands"
}

@Article{	  nes05a,
  author	= "Nesterov, Y.",
  title		= "Smooth minimization of nonsmooth functions",
  journal	= "Math. Program.",
  year		= 2005,
  volume	= 103,
  page		= {127--152}
}

###Article{	  nes05a,
  author	= "Nesterov, Y.",
  title		= "Smooth minimization of nonsmooth functions",
  journal	= mathprog,
  year		= 2005,
  volume	= 103,
  page		= {127--152}
}

###Article{	  nes05a,
  author	= "Nesterov, Y.",
  title		= "Smooth minimization of nonsmooth functions",
  journal	= mathprog,
  year		= 2005,
  volume	= 103,
  page		= {127--152}
}

@Article{	  nes05b,
  author	= "Nesterov, Y.",
  title		= "Excessive gap technique in nonsmooth convex minimization",
  journal	= siamopt,
  year		= 2005,
  volume	= 16,
  page		= {235--249}
}

###Article{	  nes05b,
  author	= "Nesterov, Y.",
  title		= "Excessive gap technique in nonsmooth convex minimization",
  journal	= siamopt,
  year		= 2005,
  volume	= 16,
  page		= {235--249}
}

###Article{	  nes05b,
  author	= "Nesterov, Y.",
  title		= "Excessive gap technique in nonsmooth convex minimization",
  journal	= siamopt,
  year		= 2005,
  volume	= 16,
  page		= {235--249}
}

@TechReport{	  nes07,
  title		= {Gradient methods for minimizing composite objective
		  function},
  author	= {Nesterov, Y.},
  institution	= {Center for Operations Research and Econometrics (CORE),
		  Catholic University of Louvain},
  type		= {Tech. Rep.},
  volume	= 76,
  year		= 2007
}

###TechReport{	  nes07,
  title		= {Gradient methods for minimizing composite objective
		  function},
  author	= {Nesterov, Y.},
  institution	= {Center for Operations Research and Econometrics (CORE),
		  Catholic University of Louvain},
  type		= {Tech. Rep.},
  volume	= 76,
  year		= 2007
}

###TechReport{	  nes07,
  title		= {Gradient methods for minimizing composite objective
		  function},
  author	= {Nesterov, Y.},
  institution	= {Center for Operations Research and Econometrics (CORE),
		  Catholic University of Louvain},
  type		= {Tech. Rep.},
  volume	= 76,
  year		= 2007
}

@Article{	  nes07a,
  author	= "Nesterov, Y.",
  title		= "Dual extrapolation and its applications to solving
		  variational inequalities and related problems",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 109,
  page		= {319-344}
}

###Article{	  nes07a,
  author	= "Nesterov, Y.",
  title		= "Dual extrapolation and its applications to solving
		  variational inequalities and related problems",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 109,
  page		= {319-344}
}

###Article{	  nes07a,
  author	= "Nesterov, Y.",
  title		= "Dual extrapolation and its applications to solving
		  variational inequalities and related problems",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 109,
  page		= {319-344}
}

@TechReport{	  nes07b,
  author	= "Nesterov, Y.",
  title		= "Gradient methods for minimizing composite objective
		  function",
  institution	= "CORE, Catholic University of Louvain",
  address	= "Louvain-la-Neuve, Belgium",
  year		= 2007,
  month		= "September"
}

###TechReport{	  nes07b,
  author	= "Nesterov, Y.",
  title		= "Gradient methods for minimizing composite objective
		  function",
  institution	= "CORE, Catholic University of Louvain",
  address	= "Louvain-la-Neuve, Belgium",
  year		= 2007,
  month		= "September"
}

###TechReport{	  nes07b,
  author	= "Nesterov, Y.",
  title		= "Gradient methods for minimizing composite objective
		  function",
  institution	= "CORE, Catholic University of Louvain",
  address	= "Louvain-la-Neuve, Belgium",
  year		= 2007,
  month		= "September"
}

@Article{	  nes07c,
  author	= "Nesterov, Y.",
  title		= "Primal-dual subgradient methods for convex problems",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 120,
  page		= {221--259},
  note		= "DOI 10.1007/s10107-007-0149-x"
}

###Article{	  nes07c,
  author	= "Nesterov, Y.",
  title		= "Primal-dual subgradient methods for convex problems",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 120,
  page		= {221--259},
  note		= "DOI 10.1007/s10107-007-0149-x"
}

###Article{	  nes07c,
  author	= "Nesterov, Y.",
  title		= "Primal-dual subgradient methods for convex problems",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 120,
  page		= {221--259},
  note		= "DOI 10.1007/s10107-007-0149-x"
}

@Article{	  nes88,
  author	= "Nesterov, Y.",
  title		= "On an approach to the construction of optimal methods of
		  minimization of smooth convex functions",
  journal	= "Ekonom. i. Mat. Metody ",
  year		= 1988,
  volume	= 24,
  page		= {509--517}
}

###Article{	  nes88,
  author	= "Nesterov, Y.",
  title		= "On an approach to the construction of optimal methods of
		  minimization of smooth convex functions",
  journal	= "Ekonom. i. Mat. Metody ",
  year		= 1988,
  volume	= 24,
  page		= {509--517}
}

###Article{	  nes88,
  author	= "Nesterov, Y.",
  title		= "On an approach to the construction of optimal methods of
		  minimization of smooth convex functions",
  journal	= "Ekonom. i. Mat. Metody ",
  year		= 1988,
  volume	= 24,
  page		= {509--517}
}

@Article{	  nes:1983,
  author	= "Nesterov, Y.",
  title		= "A method for unconstrained convex minimization problem
		  with the rate of convergence {$O(1/k^2)$}",
  journal	= "Soviet Math. Dokl.",
  year		= 1983,
  volume	= 269,
  page		= {543--547}
}

###Article{	  nes:1983,
  author	= "Nesterov, Y.",
  title		= "A method for unconstrained convex minimization problem
		  with the rate of convergence {$O(1/k^2)$}",
  journal	= "Soviet Math. Dokl.",
  year		= 1983,
  volume	= 269,
  page		= {543--547}
}

###Article{	  nes:1983,
  author	= "Nesterov, Y.",
  title		= "A method for unconstrained convex minimization problem
		  with the rate of convergence {$O(1/k^2)$}",
  journal	= "Soviet Math. Dokl.",
  year		= 1983,
  volume	= 269,
  page		= {543--547}
}

@Article{	  nesterov:2005,
  author	= {Y. Nesterov},
  title		= {Smooth minimization of non-smooth functions},
  journal	= mathprog,
  year		= 2005,
  volume	= 103,
  pages		= {127-152}
}

###Article{	  nesterov:2005,
  author	= {Y. Nesterov},
  title		= {Smooth minimization of non-smooth functions},
  journal	= mathprog,
  year		= 2005,
  volume	= 103,
  pages		= {127-152}
}

@Article{	  nestnemi:1992,
  author	= {Yu. E. Nesterov and A. S. Nemirovski},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= optimmeth,
  pages		= {95--115},
  title		= {Conic formulation of a convex programming problem and
		  duality},
  volume	= 1,
  year		= 1992
}

###Article{	  nestnemi:1992,
  author	= {Yu. E. Nesterov and A. S. Nemirovski},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= optimmeth,
  pages		= {95--115},
  title		= {Conic formulation of a convex programming problem and
		  duality},
  volume	= 1,
  year		= 1992
}

###Article{	  nestnemi:1992,
  author	= {Yu. E. Nesterov and A. S. Nemirovski},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= optimmeth,
  pages		= {95--115},
  title		= {Conic formulation of a convex programming problem and
		  duality},
  volume	= 1,
  year		= 1992
}

@Misc{		  netlib:2006,
  date-modified	= {2007-12-14 11:59:07 -0800},
  key		= {Netlib},
  title		= {{NETLIB} Linear Programming Library},
  url		= {\url{http://www.netlib.org/lp/infeas/}},
  year		= 2006
}

###Misc{	  netlib:2006,
  date-modified	= {2007-12-14 11:59:07 -0800},
  key		= {Netlib},
  title		= {{NETLIB} Linear Programming Library},
  url		= {\url{http://www.netlib.org/lp/infeas/}},
  year		= 2006
}

###Misc{	  netlib:2006,
  date-modified	= {2007-12-14 11:59:07 -0800},
  key		= {Netlib},
  title		= {{NETLIB} Linear Programming Library},
  url		= {\url{http://www.netlib.org/lp/infeas/}},
  year		= 2006
}

@Article{	  newman1965location,
  title		= {Location of the maximum on unimodal surfaces},
  author	= {Newman, Donald J},
  journal	= {Journal of the ACM (JACM)},
  volume	= {12},
  number	= {3},
  pages		= {395--398},
  year		= {1965},
  publisher	= {ACM}
}

@Book{		  ney83,
  author	= "Nemirovski, A. and Yudin, D.",
  title		= "Problem Complexity and Method Efficiency in Optimization",
  publisher	= "Wiley",
  year		= 1983,
  address	= "New York"
}

###Book{	  ney83,
  author	= "Nemirovski, A. and Yudin, D.",
  title		= "Problem Complexity and Method Efficiency in Optimization",
  publisher	= "Wiley",
  year		= 1983,
  address	= "New York"
}

###Book{	  ney83,
  author	= "Nemirovski, A. and Yudin, D.",
  title		= "Problem Complexity and Method Efficiency in Optimization",
  publisher	= "Wiley",
  year		= 1983,
  address	= "New York"
}

@TechReport{	  nie06,
  author	= "Nie, J.",
  title		= "Sum of squares method for sensor network localization",
  institution	= "Department of Mathematics, University of California",
  address	= "Berkeley",
  year		= 2006,
  month		= "June",
  note		= "to appear in Comput. Optim. Appl."
}

###TechReport{	  nie06,
  author	= "Nie, J.",
  title		= "Sum of squares method for sensor network localization",
  institution	= "Department of Mathematics, University of California",
  address	= "Berkeley",
  year		= 2006,
  month		= "June",
  note		= "to appear in Comput. Optim. Appl."
}

###TechReport{	  nie06,
  author	= "Nie, J.",
  title		= "Sum of squares method for sensor network localization",
  institution	= "Department of Mathematics, University of California",
  address	= "Berkeley",
  year		= 2006,
  month		= "June",
  note		= "to appear in Comput. Optim. Appl."
}

@InCollection{	  nips2012_4523,
  title		= {A quasi-Newton proximal splitting method},
  author	= {Becker, Stephen and Fadili, Jalal},
  booktitle	= {Advances in Neural Information Processing Systems 25},
  editor	= {F. Pereira and C. J. C. Burges and L. Bottou and K. Q.
		  Weinberger},
  pages		= {2618--2626},
  year		= 2012,
  publisher	= {Curran Associates, Inc.}
}

###InCollection{  nips2012_4523,
  title		= {A quasi-{Newton} proximal splitting method},
  author	= {Becker, Stephen and Fadili, Jalal},
  booktitle	= {Advances in Neural Information Processing Systems 25},
  editor	= {F. Pereira and C.J.C. Burges and L. Bottou and K.Q.
		  Weinberger},
  pages		= {2618--2626},
  year		= 2012,
  publisher	= {Curran Associates, Inc.}
}

@InProceedings{	  nips2012_4797,
  title		= {Stochastic Gradient Descent with Only One Projection},
  author	= {Mahdavi, Mehrdad and Yang, Tianbao and Jin, Rong and
		  Shenghuo Zhu and Jinfeng Yi},
  booktitle	= {Advances in Neural Information Processing Systems 25},
  editor	= {F. Pereira and C.J.C. Burges and L. Bottou and K.Q.
		  Weinberger},
  pages		= {494--502},
  year		= 2012,
  url		= {http://papers.nips.cc/paper/4797-stochastic-gradient-descent-with-only-one-projection.pdf}
}

###InProceedings{ nips2012_4797,
  title		= {Stochastic Gradient Descent with Only One Projection},
  author	= {Mahdavi, Mehrdad and Yang, Tianbao and Jin, Rong and
		  Shenghuo Zhu and Jinfeng Yi},
  booktitle	= {Advances in Neural Information Processing Systems 25},
  editor	= {F. Pereira and C.J.C. Burges and L. Bottou and K.Q.
		  Weinberger},
  pages		= {494--502},
  year		= 2012,
  url		= {http://papers.nips.cc/paper/4797-stochastic-gradient-descent-with-only-one-projection.pdf}
}

@InCollection{	  nips2013_4936,
  author	= {Ke Hou and Zirui Zhou and Anthony Man-Cho So and Zhi-Quan
		  Luo},
  booktitle	= {Advances in Neural Information Processing Systems 26},
  pages		= {710--718},
  title		= {On the Linear Convergence of the Proximal Gradient Method
		  for Trace Norm Regularization},
  url		= {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/417.pdf},
  year		= {2013},
  bdsk-url-1	= {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/417.pdf}
}

###InCollection{  nips2013_4936,
  author	= {Ke Hou and Zirui Zhou and Anthony Man-Cho So and Zhi-Quan
		  Luo},
  booktitle	= {Advances Neural Inform. Processing Systems 26},
  pages		= {710--718},
  title		= {On the Linear Convergence of the Proximal Gradient Method
		  for Trace Norm Regularization},
  url		= {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/417.pdf},
  year		= {2013},
  bdsk-url-1	= {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/417.pdf}
}

###InCollection{  nips2013_4936,
  author	= {Ke Hou and Zirui Zhou and Anthony Man-Cho So and Zhi-Quan
		  Luo},
  booktitle	= {Advances in Neural Information Processing Systems 26},
  pages		= {710--718},
  title		= {On the Linear Convergence of the Proximal Gradient Method
		  for Trace Norm Regularization},
  url		= {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/417.pdf},
  year		= {2013},
  bdsk-url-1	= {http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/417.pdf}
}

@InCollection{	  nips2013_5041,
  author	= {Netrapalli, Praneeth and Jain, Prateek and Sanghavi,
		  Sujay},
  booktitle	= {Advances in Neural Information Processing Systems 26},
  editor	= {C.J.C. Burges and L. Bottou and M. Welling and Z.
		  Ghahramani and K.Q. Weinberger},
  pages		= {2796--2804},
  publisher	= {Curran Associates, Inc.},
  title		= {Phase Retrieval using Alternating Minimization},
  year		= 2013,
  url		= {http://papers.nips.cc/paper/5041-pha}
}

###InCollection{  nips2013_5041,
  author	= {Netrapalli, Praneeth and Jain, Prateek and Sanghavi,
		  Sujay},
  booktitle	= {Advances in Neural Information Processing Systems 26},
  editor	= {C.J.C. Burges and L. Bottou and M. Welling and Z.
		  Ghahramani and K.Q. Weinberger},
  pages		= {2796--2804},
  publisher	= {Curran Associates, Inc.},
  title		= {Phase Retrieval using Alternating Minimization},
  year		= 2013,
  url		= {http://papers.nips.cc/paper/5041-pha}
}

@InCollection{	  nips2014_5384,
  publisher	= {Curran Associates, Inc.},
  author	= {Kai Zhong and En-hsu Yen and Inderjit S. Dhillon and
		  Pradeep K. Ravikumar},
  booktitle	= {Advances Neural Inform. Processing Systems 27},
  title		= {Proximal Quasi-Newton for Computationally Intensive
		  L1-regularized M-estimators},
  editor	= {Z. Ghahramani and M. Welling and C. Cortes and N.d.
		  Lawrence and K.q. Weinberger},
  year		= 2014,
  pages		= {2375--2383}
}

###InCollection{  nips2014_5384,
  title		= {Proximal Quasi-{Newton} for Computationally Intensive
		  L1-regularized M-estimators},
  author	= {Zhong, Kai and Yen, En-Hsu and Dhillon, Inderjit S and
		  Ravikumar, Pradeep K},
  booktitle	= {Advances in Neural Information Processing Systems 27},
  editor	= {Z. Ghahramani and M. Welling and C. Cortes and N.D.
		  Lawrence and K.Q. Weinberger},
  pages		= {2375--2383},
  year		= 2014,
  publisher	= {Curran Associates, Inc.}
}

@Misc{		  nist:chemwebbook,
  author	= {{National Institute of Standards and Technology}},
  title		= {{NIST Chemistry WebBook}},
  howpublished	= {\url{http://webbook.nist.gov/chemistry/}},
  url		= {http://webbook.nist.gov/chemistry/},
  year		= 2009
}

###Misc{	  nist:chemwebbook,
  author	= {{National Institute of Standards and Technology}},
  title		= {{NIST Chemistry WebBook}},
  howpublished	= {\url{http://webbook.nist.gov/chemistry/}},
  url		= {http://webbook.nist.gov/chemistry/},
  year		= 2009
}

###Misc{	  nist:chemwebbook,
  author	= {{National Institute of Standards and Technology}},
  title		= {{NIST Chemistry WebBook}},
  howpublished	= {\url{http://webbook.nist.gov/chemistry/}},
  url		= {http://webbook.nist.gov/chemistry/},
  year		= 2009
}

@Book{		  nocedal1999,
  author	= "Nocedal, J. and Wright S. J.",
  title		= "Numerical Optimization",
  publisher	= "Springer-Verlag",
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}

###Book{	  nocedal1999,
  author	= "Nocedal, J. and Wright S. J.",
  title		= "Numerical Optimization",
  publisher	= "Springer-Verlag",
  year		= 1999,
  address	= "New York"
}

###Book{	  nocedal1999,
  author	= "Nocedal, J. and Wright S. J.",
  title		= "Numerical Optimization",
  publisher	= "Springer-Verlag",
  year		= 1999,
  address	= "New York"
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@Book{		  nocewrig:2006,
  address	= {New York},
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  edition	= {Second},
  publisher	= {Springer},
  title		= {Numerical Optimization},
  year		= 2006
}

###Book{	  nocewrig:2006,
  address	= {New York},
  author	= {J. Nocedal and S. J. Wright},
  edition	= {Second},
  publisher	= {Springer},
  title		= {Numerical Optimization},
  year		= 2006
}

###Book{	  nocewrig:2006,
  address	= {New York},
  author	= {J. Nocedal and S. J. Wright},
  edition	= {Second},
  publisher	= {Springer},
  title		= {Numerical Optimization},
  year		= 2006
}

@Book{		  now99,
  address	= {New York},
  author	= {Jorge Nocedal and Stephen J. Wright},
  publisher	= {Springer},
  title		= {Numerical Optimization},
  year		= 1999
}

###Book{	  now99,
  address	= {New York},
  author	= {Jorge Nocedal and Stephen J. Wright},
  publisher	= {Springer},
  title		= {Numerical Optimization},
  year		= 1999
}

###Book{	  now99,
  address	= {New York},
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  publisher	= {Springer},
  title		= {Numerical Optimization},
  year		= 1999
}

@Article{	  obgx05,
  author	= "Osher, S. and Burger, M. and Goldfarb, D. and Xu. J.",
  title		= "An iterative regularization method for total
		  variation-based image restoration",
  journal	= siammms,
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###Article{	  obgx05,
  author	= "Osher, S. and Burger, M. and Goldfarb, D. and Xu. J.",
  title		= "An iterative regularization method for total
		  variation-based image restoration",
  journal	= siammms,
  year		= 2005,
  volume	= 4,
  page		= {460--489}
}

###Article{	  obgx05,
  author	= "Osher, S. and Burger, M. and Goldfarb, D. and Xu. J.",
  title		= "An iterative regularization method for total
		  variation-based image restoration",
  journal	= siammms,
  year		= 2005,
  volume	= 4,
  page		= {460--489}
}

@Article{	  olivsore:2005,
  author	= {A.R.L. Oliveira and D.C. Sorensen},
  title		= {A new class of preconditioners for large-scale linear
		  systems from interior point methods for linear
		  programming},
  journal	= linalgapp,
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  volume	= 394,
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}

###Article{	  olivsore:2005,
  author	= {A.R.L. Oliveira and D.C. Sorensen},
  title		= {A new class of preconditioners for large-scale linear
		  systems from interior point methods for linear
		  programming},
  journal	= linalgapp,
  year		= 2005,
  volume	= 394,
  pages		= {1--24}
}

###Article{	  olivsore:2005,
  author	= {A.R.L. Oliveira and D.C. Sorensen},
  title		= {A new class of preconditioners for large-scale linear
		  systems from interior point methods for linear
		  programming},
  journal	= linalgapp,
  year		= 2005,
  volume	= 394,
  pages		= {1--24}
}

@Book{		  orr70,
  address	= {London},
  author	= {J. M. Ortega and W. C. Rheinboldt},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= academic,
  title		= {Iterative Solutions of Nonlinear Equations in Several
		  Variables},
  year		= 1970
}

###Book{	  orr70,
  address	= {London},
  author	= {J. M. Ortega and W. C. Rheinboldt},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= academic,
  title		= {Iterative Solutions of Nonlinear Equations in Several
		  Variables},
  year		= 1970
}

###Book{	  orr70,
  address	= {London},
  author	= {J. M. Ortega and W. C. Rheinboldt},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= academic,
  title		= {Iterative Solutions of Nonlinear Equations in Several
		  Variables},
  year		= 1970
}

@Book{		  ortega2000,
  author	= "Ortega, J. M. and Rheinboldt, W. C.",
  title		= "Iterative Solution of Nonlinear Equations in Several
		  Variables",
  publisher	= "SIAM",
  year		= 2000,
  address	= "Philadelphia"
}

###Book{	  ortega2000,
  author	= "Ortega, J. M. and Rheinboldt, W. C.",
  title		= "Iterative Solution of Nonlinear Equations in Several
		  Variables",
  publisher	= "SIAM",
  year		= 2000,
  address	= "Philadelphia"
}

###Book{	  ortega2000,
  author	= "Ortega, J. M. and Rheinboldt, W. C.",
  title		= "Iterative Solution of Nonlinear Equations in Several
		  Variables",
  publisher	= "SIAM",
  year		= 2000,
  address	= "Philadelphia"
}

@Article{	  osbopresturl:2000a,
  author	= {M. R. Osborne and Brett Presnell and B. A. Turlach},
  date-modified	= {2007-09-14 11:00:08 -0700},
  journal	= imanumerana,
  keywords	= {L1},
  local-url	= {file://localhost/Users/mpf/papers/OsboPresTurl00.pdf},
  number	= 3,
  pages		= {389--403},
  title		= {A new approach to variable selection in least squares
		  problems},
  volume	= 20,
  year		= 2000
}

###Article{	  osbopresturl:2000a,
  author	= {M. R. Osborne and Brett Presnell and B. A. Turlach},
  date-modified	= {2007-09-14 11:00:08 -0700},
  journal	= imanumerana,
  keywords	= {L1},
  local-url	= {file://localhost/Users/mpf/papers/OsboPresTurl00.pdf},
  number	= 3,
  pages		= {389--403},
  title		= {A new approach to variable selection in least squares
		  problems},
  volume	= 20,
  year		= 2000
}

###Article{	  osbopresturl:2000a,
  author	= {M. R. Osborne and Brett Presnell and B. A. Turlach},
  date-modified	= {2007-09-14 11:00:08 -0700},
  journal	= imanumerana,
  keywords	= {L1},
  local-url	= {file://localhost/Users/mpf/papers/OsboPresTurl00.pdf},
  number	= 3,
  pages		= {389--403},
  title		= {A new approach to variable selection in least squares
		  problems},
  volume	= 20,
  year		= 2000
}

@Article{	  osbopresturl:2000b,
  author	= {M. R. Osborne and B. Presnell and B. A. Turlach},
  date-modified	= {2007-09-14 11:00:11 -0700},
  journal	= {J. of Comput. Graph. Statist.},
  keywords	= {L1},
  local-url	= {file://localhost/Users/mpf/papers/OsboPresnTurl99.pdf},
  pages		= {319--337},
  title		= {On the {LASSO} and its dual},
  volume	= 9,
  year		= 2000
}

###Article{	  osbopresturl:2000b,
  author	= {M. R. Osborne and B. Presnell and B. A. Turlach},
  date-modified	= {2007-09-14 11:00:11 -0700},
  journal	= {J. of Comput. Graph. Statist.},
  keywords	= {L1},
  local-url	= {file://localhost/Users/mpf/papers/OsboPresnTurl99.pdf},
  pages		= {319--337},
  title		= {On the {LASSO} and its dual},
  volume	= 9,
  year		= 2000
}

###Article{	  osbopresturl:2000b,
  author	= {M. R. Osborne and B. Presnell and B. A. Turlach},
  date-modified	= {2007-09-14 11:00:11 -0700},
  journal	= {J. of Comput. Graph. Statist.},
  keywords	= {L1},
  local-url	= {file://localhost/Users/mpf/papers/OsboPresnTurl99.pdf},
  pages		= {319--337},
  title		= {On the {LASSO} and its dual},
  volume	= 9,
  year		= 2000
}

@Article{	  osborne1992fisher,
  title		= {Fisher's method of scoring},
  author	= {Osborne, M.R.},
  journal	= {Intern.\@ Stat.\@ Rev.},
  pages		= {99--117},
  year		= 1992
}


@Article{	  osborne:1982,
  title		= {A Finite Algorithm for the Rank Regression Problem},
  author	= {Osborne, M. R.},
  journal	= {J. Appl. Prob.},
  volume	= 19,
  pages		= {241-252},
  abstract	= {A new approach to the minimization of polyhedral convex
		  functions is applied to give a finite algorithm for the
		  rank regression problem. Numerical results for the Daniel
		  and Wood example are presented.},
  year		= 1982,
  publisher	= {Applied Probability Trust}
}

###Article{	  osborne:1982,
  title		= {A Finite Algorithm for the Rank Regression Problem},
  author	= {Osborne, M. R.},
  journal	= {J. Appl. Prob.},
  volume	= 19,
  pages		= {241-252},
  abstract	= {A new approach to the minimization of polyhedral convex
		  functions is applied to give a finite algorithm for the
		  rank regression problem. Numerical results for the Daniel
		  and Wood example are presented.},
  year		= 1982,
  publisher	= {Applied Probability Trust}
}

@Misc{		  osborne:2003,
  title		= {{When LP is not a good idea--using structure in polyhedral
		  optimization problems}},
  author	= {Osborne, M. R. },
  year		= 2003
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###Misc{	  osborne:2003,
  title		= {{When LP is not a good idea--using structure in polyhedral
		  optimization problems}},
  author	= {Osborne, M. R. },
  year		= 2003
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###Misc{	  osborne:2003,
  title		= {{When LP is not a good idea--using structure in polyhedral
		  optimization problems}},
  author	= {Osborne, M. R. },
  year		= 2003
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@Article{	  otj09,
  author	= "G. Obozinski and B. Taskar and M. I. Jordan",
  title		= "Joint covariate selection and joint subspace selection for
		  multiple classification problems",
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  year		= 2009
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###Article{	  otj09,
  author	= "G. Obozinski and B. Taskar and M. I. Jordan",
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  journal	= "Stat. Comput.",
  year		= 2009
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###Article{	  otj09,
  author	= "G. Obozinski and B. Taskar and M. I. Jordan",
  title		= "Joint covariate selection and joint subspace selection for
		  multiple classification problems",
  journal	= "Stat. Comput.",
  year		= 2009
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@Book{		  outrkocvzowe:1998,
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  publisher	= {Kluwer Academic},
  title		= {Nonsmooth approach to optimization problems with
		  equilibrium constraints: {T}heory, applications, and
		  numerical results},
  year		= 1998
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###Book{	  outrkocvzowe:1998,
  address	= {Dordrecht, The Netherlands},
  author	= {J. Outrata and M. Kocvara and J. Zowe},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Kluwer Academic},
  title		= {Nonsmooth approach to optimization problems with
		  equilibrium constraints: {T}heory, applications, and
		  numerical results},
  year		= 1998
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###Book{	  outrkocvzowe:1998,
  address	= {Dordrecht, The Netherlands},
  author	= {J. Outrata and M. Kocvara and J. Zowe},
  date-modified	= {2007-07-18 14:59:33 -0700},
  publisher	= {Kluwer Academic},
  title		= {Nonsmooth approach to optimization problems with
		  equilibrium constraints: {T}heory, applications, and
		  numerical results},
  year		= 1998
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@InProceedings{	  ouyanggray:2012,
  title		= {Stochastic Smoothing for Nonsmooth Minimizations:
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###InProceedings{ ouyanggray:2012,
  title		= {Stochastic Smoothing for Nonsmooth Minimizations:
		  Accelerating {SGD} by Exploiting Structure},
  author	= {H. Ouyang and A. Gray},
  booktitle	= {Proc. 29th Intern. Conf. Machine Learning (ICML-12)},
  year		= 2012
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@Article{	  paattapp:1994,
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###Article{	  paattapp:1994,
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  journal	= {Envirometrics},
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  title		= {Positive matrix factorization: A non-negative factor model
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  volume	= 5,
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###Article{	  paattapp:1994,
  author	= {P. Paatero and U. Tapper},
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  journal	= {Envirometrics},
  pages		= {111--126},
  title		= {Positive matrix factorization: A non-negative factor model
		  with optimal utilization of error},
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  year		= 1994
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@InProceedings{	  pacheco2012minimization,
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###InProceedings{ pacheco2012minimization,
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  booktitle	= {Advances in Neural Information Processing Systems},
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@Article{	  pah07,
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###Article{	  pah07,
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###Article{	  pah07,
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@Article{	  paigsaun:1975,
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###Article{	  paigsaun:1975,
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###Article{	  paigsaun:1975,
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  year		= 1975,
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@Article{	  paigsaun:1982,
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  title		= {{LSQR}: An Algorithm for Sparse Linear Equations and
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  year		= 1982
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###Article{	  paigsaun:1982,
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  pages		= {43--71},
  title		= {{LSQR}: An Algorithm for Sparse Linear Equations and
		  Sparse Least Squares},
  volume	= 8,
  year		= 1982
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###Article{	  paigsaun:1982,
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  journal	= acmmathsoft,
  pages		= {43--71},
  title		= {{LSQR}: An Algorithm for Sparse Linear Equations and
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  volume	= 8,
  year		= 1982
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@Article{	  palalasdengq:1982,
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  title		= {Nonlinear optimization by successive linear programming},
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###Article{	  palalasdengq:1982,
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  journal	= managesci,
  month		= {October},
  number	= 10,
  pages		= {1106--1120},
  title		= {Nonlinear optimization by successive linear programming},
  volume	= 28,
  year		= 1982
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###Article{	  palalasdengq:1982,
  author	= {F. Palacios-Gomez and L. Lasdon and E. Engquist},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= managesci,
  month		= {October},
  number	= 10,
  pages		= {1106--1120},
  title		= {Nonlinear optimization by successive linear programming},
  volume	= 28,
  year		= 1982
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@Article{	  pan87,
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###Article{	  pan87,
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		  variational inequality problem",
  journal	= "Math. Oper. Res.",
  year		= 1987,
  volume	= 12,
  page		= {474--484}
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###Article{	  pan87,
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  title		= "A posteriori error bounds for the linearly constrained
		  variational inequality problem",
  journal	= "Math. Oper. Res.",
  year		= 1987,
  volume	= 12,
  page		= {474--484}
}

@Article{	  pan:2005,
  author	= {Ping-Qi Pan},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {49-68},
  publisher	= {SIAM},
  title		= {A Revised Dual Projective Pivot Algorithm for Linear
		  Programming},
  volume	= 16,
  year		= 2005
}

###Article{	  pan:2005,
  author	= {Ping-Qi Pan},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {49-68},
  publisher	= {SIAM},
  title		= {A Revised Dual Projective Pivot Algorithm for Linear
		  Programming},
  volume	= 16,
  year		= 2005
}

###Article{	  pan:2005,
  author	= {Ping-Qi Pan},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamopt,
  number	= 1,
  pages		= {49-68},
  publisher	= {SIAM},
  title		= {A Revised Dual Projective Pivot Algorithm for Linear
		  Programming},
  volume	= 16,
  year		= 2005
}

@Article{	  parikh2013proximal,
  title		= {Proximal algorithms},
  author	= {Parikh, Neal and Boyd, Stephen},
  journal	= {Foundations and Trends in Optimization},
  volume	= 1,
  number	= 3,
  pages		= {123--231},
  year		= 2013
}

###Article{	  parikh2013proximal,
  title		= {Proximal algorithms},
  author	= {Parikh, Neal and Boyd, Stephen},
  journal	= {Foundations and Trends in Optimization},
  volume	= 1,
  number	= 3,
  pages		= {123--231},
  year		= 2013
}

@Misc{		  pascalchallenge2016,
  key		= {{Pascal Large Scale Learning Challenge}},
  title		= {},
  howpublished	= {\url{http://largescale.ml.tu-berlin.de/instructions}},
  note		= {Accessed: 2016-11-22},
  year		= {2016}
}

@TechReport{	  pataschm:2002,
  author	= {G. Pataki and S. Schmieta},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computational Optimization Research Center, Columbia
		  University, New York},
  number	= {Preliminary draft},
  title		= {The {DIMACS} library of semidefinite-quadratic-linear
		  programs},
  year		= {July 2002}
}

###TechReport{	  pataschm:2002,
  author	= {G. Pataki and S. Schmieta},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computational Optimization Research Center, Columbia
		  University, New York},
  number	= {Preliminary draft},
  title		= {The {DIMACS} library of semidefinite-quadratic-linear
		  programs},
  year		= {July 2002}
}

###TechReport{	  pataschm:2002,
  author	= {G. Pataki and S. Schmieta},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computational Optimization Research Center, Columbia
		  University, New York},
  number	= {Preliminary draft},
  title		= {The {DIMACS} library of semidefinite-quadratic-linear
		  programs},
  year		= {July 2002}
}

@InProceedings{	  patirezakris:1993,
  abstract	= {In this paper we describe a recursive algorithm to compute
		  representations of functions with respect to nonorthogonal
		  and possibly overcomplete dictionaries of elementary
		  building blocks e.g. affine (wavelet) frames. We propose a
		  modification to the Matching Pursuit algorithm of Mallat
		  and Zhang (1992) that maintains full backward orthogonality
		  of the residual (error) at every step and thereby leads to
		  improved convergence. We refer to this modified algorithm
		  as Orthogonal Matching Pursuit (OMP). It is shown that all
		  additional computation required for the OMP algorithm may
		  be performed recursively.},
  author	= {Y. C. Pati and R. Rezaiifar and P. S. Krishnaprasad},
  booktitle	= {Proceedings of the 27th Annual Asilomar Conference on
		  Signals, Systems and Computers},
  month		= {Nov.},
  pages		= {40--44},
  title		= {Orthogonal Matching Pursuit: Recursive Function
		  Approximation with Applications to Wavelet Decomposition},
  volume	= 1,
  year		= 1993
}

###InProceedings{ patirezakris:1993,
  abstract	= {In this paper we describe a recursive algorithm to compute
		  representations of functions with respect to nonorthogonal
		  and possibly overcomplete dictionaries of elementary
		  building blocks e.g. affine (wavelet) frames. We propose a
		  modification to the Matching Pursuit algorithm of Mallat
		  and Zhang (1992) that maintains full backward orthogonality
		  of the residual (error) at every step and thereby leads to
		  improved convergence. We refer to this modified algorithm
		  as Orthogonal Matching Pursuit (OMP). It is shown that all
		  additional computation required for the OMP algorithm may
		  be performed recursively.},
  author	= {Y. C. Pati and R. Rezaiifar and P. S. Krishnaprasad},
  booktitle	= {Proceedings of the 27th Annual Asilomar Conference on
		  Signals, Systems and Computers},
  month		= {Nov.},
  pages		= {40--44},
  title		= {Orthogonal Matching Pursuit: Recursive Function
		  Approximation with Applications to Wavelet Decomposition},
  volume	= 1,
  year		= 1993
}

###InProceedings{ patirezakris:1993,
  abstract	= {In this paper we describe a recursive algorithm to compute
		  representations of functions with respect to nonorthogonal
		  and possibly overcomplete dictionaries of elementary
		  building blocks e.g. affine (wavelet) frames. We propose a
		  modification to the Matching Pursuit algorithm of Mallat
		  and Zhang (1992) that maintains full backward orthogonality
		  of the residual (error) at every step and thereby leads to
		  improved convergence. We refer to this modified algorithm
		  as Orthogonal Matching Pursuit (OMP). It is shown that all
		  additional computation required for the OMP algorithm may
		  be performed recursively.},
  author	= {Y. C. Pati and R. Rezaiifar and P. S. Krishnaprasad},
  booktitle	= {Proceedings of the 27th Annual Asilomar Conference on
		  Signals, Systems and Computers},
  month		= {Nov.},
  pages		= {40--44},
  title		= {Orthogonal Matching Pursuit: Recursive Function
		  Approximation with Applications to Wavelet Decomposition},
  volume	= 1,
  year		= 1993
}

@Article{	  pere:1967,
  author	= {Victor Pereyra},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamnumanal,
  pages		= {508--533},
  title		= {Accelerating the Convergence of Discretization
		  Algorithms},
  volume	= 4,
  year		= 1967
}

###Article{	  pere:1967,
  author	= {Victor Pereyra},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamnumanal,
  pages		= {508--533},
  title		= {Accelerating the Convergence of Discretization
		  Algorithms},
  volume	= 4,
  year		= 1967
}

###Article{	  pere:1967,
  author	= {Victor Pereyra},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= siamnumanal,
  pages		= {508--533},
  title		= {Accelerating the Convergence of Discretization
		  Algorithms},
  volume	= 4,
  year		= 1967
}

@Article{	  piet:1969,
  author	= {T. Pietrzykowski},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamnumanal,
  pages		= {262--304},
  title		= {An exact potential method for constrained maxima},
  volume	= 6,
  year		= 1969
}

###Article{	  piet:1969,
  author	= {T. Pietrzykowski},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamnumanal,
  pages		= {262--304},
  title		= {An exact potential method for constrained maxima},
  volume	= 6,
  year		= 1969
}

###Article{	  piet:1969,
  author	= {T. Pietrzykowski},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamnumanal,
  pages		= {262--304},
  title		= {An exact potential method for constrained maxima},
  volume	= 6,
  year		= 1969
}

@InCollection{	  pillogrippo:1984,
  author	= {G. Di Pillo and L. Grippo},
  title		= {A class of continuously differentiable exact penalty
		  function algorithms for nonlinear programming problems},
  booktitle	= {System Modelling and Optimization},
  editor	= {E. P. Toft-Christensen},
  publisher	= {Springer-Verlag},
  address	= {Berlin},
  year		= 1984,
  pages		= {246–256}
}

###InCollection{  pillogrippo:1984,
  author	= {G. Di Pillo and L. Grippo},
  title		= {A class of continuously differentiable exact penalty
		  function algorithms for nonlinear programming problems},
  booktitle	= {System Modelling and Optimization},
  editor	= {E. P. Toft-Christensen},
  publisher	= {Springer-Verlag},
  address	= {Berlin},
  year		= 1984,
  pages		= {246–256}
}

###InCollection{  pillogrippo:1984,
  author	= {G. Di Pillo and L. Grippo},
  title		= {A class of continuously differentiable exact penalty
		  function algorithms for nonlinear programming problems},
  booktitle	= {System Modelling and Optimization},
  editor	= {E. P. Toft-Christensen},
  publisher	= {Springer-Verlag},
  address	= {Berlin},
  year		= 1984,
  pages		= {246–256}
}

@InCollection{	  pla98,
  author	= {J. Platt},
  title		= {Fast training of support vector machines using sequential
		  minimal optimization},
  booktitle	= {Advances in Kernel Methods: Support Vector Learning},
  year		= 1998,
  publisher	= {MIT Press},
  address	= {Cambridge, MA, USA}
}

###InCollection{  pla98,
  author	= {J. Platt},
  title		= {Fast training of support vector machines using sequential
		  minimal optimization},
  booktitle	= {Advances in Kernel Methods: Support Vector Learning},
  year		= 1998,
  publisher	= {MIT Press},
  address	= {Cambridge, MA, USA}
}

###InCollection{  pla98,
  author	= {J. Platt},
  title		= {Fast training of support vector machines using sequential
		  minimal optimization},
  booktitle	= {Advances in Kernel Methods: Support Vector Learning},
  year		= 1998,
  publisher	= {MIT Press},
  address	= {Cambridge, MA, USA}
}

@InCollection{	  plumbley:2006,
  author	= {Mark D. Plumbley},
  title		= {Recovery of Sparse Representations by Polytope Faces
		  Pursuit},
  booktitle	= {Independent Component Analysis and Blind Signal
		  Separation},
  pages		= {206-213},
  publisher	= {Springer},
  year		= 2006,
  volume	= {3889/2006},
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  doi		= {10.1007/11679363}
}

###InCollection{  plumbley:2006,
  author	= {Mark D. Plumbley},
  title		= {Recovery of Sparse Representations by Polytope Faces
		  Pursuit},
  booktitle	= {Independent Component Analysis and Blind Signal
		  Separation},
  pages		= {206-213},
  publisher	= {Springer},
  year		= 2006,
  volume	= {3889/2006},
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  doi		= {10.1007/11679363}
}

###InCollection{  plumbley:2006,
  author	= {Mark D. Plumbley},
  title		= {Recovery of Sparse Representations by Polytope Faces
		  Pursuit},
  booktitle	= {Independent Component Analysis and Blind Signal
		  Separation},
  pages		= {206-213},
  publisher	= {Springer},
  year		= 2006,
  volume	= {3889/2006},
  series	= {Lecture Notes in Computer Science},
  address	= {Berlin},
  doi		= {10.1007/11679363}
}

@Book{		  pol87,
  author	= {Polyak, B. T.},
  title		= {Introduction to optimization},
  publisher	= {Optimization Software},
  year		= 1987,
  address	= {New York}
}

###Book{	  pol87,
  author	= {Polyak, B. T.},
  title		= {Introduction to optimization},
  publisher	= {Optimization Software},
  year		= 1987,
  address	= {New York}
}

###Book{	  pol87,
  author	= {Polyak, B. T.},
  title		= {Introduction to optimization},
  publisher	= {Optimization Software},
  year		= 1987,
  address	= {New York}
}

@Article{	  pola:1976,
  author	= {Elijah Polak},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Automica},
  pages		= {337-342},
  title		= {On the global stabalization of locally convergent
		  algorithms for optimization and root finding},
  volume	= 12,
  year		= 1976
}

###Article{	  pola:1976,
  author	= {Elijah Polak},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Automica},
  pages		= {337-342},
  title		= {On the global stabalization of locally convergent
		  algorithms for optimization and root finding},
  volume	= 12,
  year		= 1976
}

###Article{	  pola:1976,
  author	= {Elijah Polak},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Automica},
  pages		= {337-342},
  title		= {On the global stabalization of locally convergent
		  algorithms for optimization and root finding},
  volume	= 12,
  year		= 1976
}

@Book{		  pola:1991,
  address	= {New York},
  author	= {Elijah Polak},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. E. Marsden and L. Sirovich},
  number	= 124,
  publisher	= springer,
  series	= {Applied Mathematical Sciences},
  title		= {Optimization: {A}lgorithms and Consistent Approximations},
  year		= 1991
}

###Book{	  pola:1991,
  address	= {New York},
  author	= {Elijah Polak},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. E. Marsden and L. Sirovich},
  number	= 124,
  publisher	= springer,
  series	= {Applied Mathematical Sciences},
  title		= {Optimization: {A}lgorithms and Consistent Approximations},
  year		= 1991
}

###Book{	  pola:1991,
  address	= {New York},
  author	= {Elijah Polak},
  date-modified	= {2007-07-18 14:59:33 -0700},
  editor	= {J. E. Marsden and L. Sirovich},
  number	= 124,
  publisher	= springer,
  series	= {Applied Mathematical Sciences},
  title		= {Optimization: {A}lgorithms and Consistent Approximations},
  year		= 1991
}

@Article{	  polyak1973method,
  title		= {The method of penalty bounds for constrained extremum
		  problems},
  author	= {Polyak, BT and Tretyakov, NV},
  journal	= {Zh. Vych. Mat i Mat. Fiz},
  volume	= {13},
  pages		= {34--46},
  year		= {1973}
}

###Article{	  polyak1973method,
  title		= {The method of penalty bounds for constrained extremum
		  problems},
  author	= {Polyak, BT and Tretyakov, NV},
  journal	= {Zh. Vych. Mat i Mat. Fiz},
  volume	= {13},
  pages		= {34--46},
  year		= {1973}
}

@TechReport{	  pot09,
  author	= "Pong, T. K. and Tseng, P.",
  title		= "(Robust) edge-based semidefinite programming relaxation of
		  sensor network localization",
  institution	= "Department of Mathematics, University of Washington",
  address	= "Seattle",
  year		= 2009,
  month		= "January"
}

###TechReport{	  pot09,
  author	= "Pong, T. K. and Tseng, P.",
  title		= "(Robust) edge-based semidefinite programming relaxation of
		  sensor network localization",
  institution	= "Department of Mathematics, University of Washington",
  address	= "Seattle",
  year		= 2009,
  month		= "January"
}

###TechReport{	  pot09,
  author	= "Pong, T. K. and Tseng, P.",
  title		= "(Robust) edge-based semidefinite programming relaxation of
		  sensor network localization",
  institution	= "Department of Mathematics, University of Washington",
  address	= "Seattle",
  year		= 2009,
  month		= "January"
}

@InCollection{	  powe:1969,
  address	= {London and New York},
  author	= {M. J. D. Powell},
  booktitle	= {Optimization},
  chapter	= 19,
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {R. Fletcher},
  publisher	= academic,
  title		= {A method for nonlinear constraints in minimization
		  problems},
  year		= 1969
}

###InCollection{  powe:1969,
  address	= {London and New York},
  author	= {M. J. D. Powell},
  booktitle	= {Optimization},
  chapter	= 19,
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {R. Fletcher},
  publisher	= academic,
  title		= {A method for nonlinear constraints in minimization
		  problems},
  year		= 1969
}

###InCollection{  powe:1969,
  address	= {London and New York},
  author	= {M. J. D. Powell},
  booktitle	= {Optimization},
  chapter	= 19,
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {R. Fletcher},
  publisher	= academic,
  title		= {A method for nonlinear constraints in minimization
		  problems},
  year		= 1969
}

@Article{	  primak1998methods,
  title		= {Methods of centers for variational inequalities and linear
		  programming},
  author	= {Primak, ME and Kheyfets, BL},
  journal	= {Applied mathematics and computation},
  volume	= {96},
  number	= {2},
  pages		= {273--293},
  year		= {1998},
  publisher	= {Elsevier}
}

@Article{	  prt08,
  author	= "Pfander, G. B. and Rauhut, H. and Tanner, J.",
  title		= "Identification of Matrices Having a Sparse Representation",
  journal	= "IEEE Trans. Signal Proc.",
  year		= 2008,
  volume	= 56,
  page		= {5376--5388}
}

###Article{	  prt08,
  author	= "Pfander, G. B. and Rauhut, H. and Tanner, J.",
  title		= "Identification of Matrices Having a Sparse Representation",
  journal	= "IEEE Trans. Signal Proc.",
  year		= 2008,
  volume	= 56,
  page		= {5376--5388}
}

###Article{	  prt08,
  author	= "Pfander, G. B. and Rauhut, H. and Tanner, J.",
  title		= "Identification of Matrices Having a Sparse Representation",
  journal	= "IEEE Trans. Signal Proc.",
  year		= 2008,
  volume	= 56,
  page		= {5376--5388}
}

@Article{	  pshe:1970,
  author	= {Borris N. Pshenichnyj},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Mathematical notes of the Academy of Sciences of the
		  USSR},
  note		= {[English translation]; Original Russian: Matematicheskie
		  Zametki, 8 (1970), pp.~635--640. (Note that the authors
		  name is transliterated as Pshenichnyi)},
  pages		= {827-830},
  title		= {{N}ewton's {M}ethod for the solution of systems of
		  equalities and inequalities},
  volume	= 8,
  year		= 1970
}

###Article{	  pshe:1970,
  author	= {Borris N. Pshenichnyj},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Mathematical notes of the Academy of Sciences of the
		  USSR},
  note		= {[English translation]; Original Russian: Matematicheskie
		  Zametki, 8 (1970), pp.~635--640. (Note that the authors
		  name is transliterated as Pshenichnyi)},
  pages		= {827-830},
  title		= {{N}ewton's {M}ethod for the solution of systems of
		  equalities and inequalities},
  volume	= 8,
  year		= 1970
}

###Article{	  pshe:1970,
  author	= {Borris N. Pshenichnyj},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= {Mathematical notes of the Academy of Sciences of the
		  USSR},
  note		= {[English translation]; Original Russian: Matematicheskie
		  Zametki, 8 (1970), pp.~635--640. (Note that the authors
		  name is transliterated as Pshenichnyi)},
  pages		= {827-830},
  title		= {{N}ewton's {M}ethod for the solution of systems of
		  equalities and inequalities},
  volume	= 8,
  year		= 1970
}

@Book{		  pshe:1994,
  address	= {Berlin},
  annote	= {Stephen S. Wilson},
  author	= {Borris N. Pshenichnyj},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= springer,
  title		= {The Linearization Method for Constrained Optimization},
  year		= 1994
}

###Book{	  pshe:1994,
  address	= {Berlin},
  annote	= {Stephen S. Wilson},
  author	= {Borris N. Pshenichnyj},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= springer,
  title		= {The Linearization Method for Constrained Optimization},
  year		= 1994
}

###Book{	  pshe:1994,
  address	= {Berlin},
  annote	= {Stephen S. Wilson},
  author	= {Borris N. Pshenichnyj},
  date-modified	= {2007-07-18 14:59:34 -0700},
  publisher	= springer,
  title		= {The Linearization Method for Constrained Optimization},
  year		= 1994
}

@TechReport{	  raghbieg:2003,
  author	= {Arvind U. Raghunathan and Lorenz T. Biegler},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Carnegie Mellon University, Department of Chemical
		  Engineering},
  title		= {Interior point methods for mathematical programs with
		  complementarity constraints},
  year		= 2003
}

###TechReport{	  raghbieg:2003,
  author	= {Arvind U. Raghunathan and Lorenz T. Biegler},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Carnegie Mellon University, Department of Chemical
		  Engineering},
  title		= {Interior point methods for mathematical programs with
		  complementarity constraints},
  year		= 2003
}

###TechReport{	  raghbieg:2003,
  author	= {Arvind U. Raghunathan and Lorenz T. Biegler},
  date-modified	= {2007-07-18 14:59:33 -0700},
  institution	= {Carnegie Mellon University, Department of Chemical
		  Engineering},
  title		= {Interior point methods for mathematical programs with
		  complementarity constraints},
  year		= 2003
}

@Article{	  ralpwrig:2000,
  author	= {Danny Ralph and Stephen J. Wright},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathofor,
  pages		= {179--194},
  title		= {Superlinear convergence of an interior-point method
		  despite dependent constraints},
  volume	= 25,
  year		= 2000
}

###Article{	  ralpwrig:2000,
  author	= {Danny Ralph and Stephen J. Wright},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathofor,
  pages		= {179--194},
  title		= {Superlinear convergence of an interior-point method
		  despite dependent constraints},
  volume	= 25,
  year		= 2000
}

###Article{	  ralpwrig:2000,
  author	= {Danny Ralph and Stephen J. Wright},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= mathofor,
  pages		= {179--194},
  title		= {Superlinear convergence of an interior-point method
		  despite dependent constraints},
  volume	= 25,
  year		= 2000
}

@TechReport{	  ralpwrig:2003,
  author	= {Danny Ralph and Stephen J. Wright},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computer Sciences, University of Wisconsin},
  month		= {December},
  number	= {03-04},
  title		= {Some properties of regularization schemes for {MPEC}s},
  year		= 2003
}

###TechReport{	  ralpwrig:2003,
  author	= {Danny Ralph and Stephen J. Wright},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computer Sciences, University of Wisconsin},
  month		= {December},
  number	= {03-04},
  title		= {Some properties of regularization schemes for {MPEC}s},
  year		= 2003
}

###TechReport{	  ralpwrig:2003,
  author	= {Danny Ralph and Stephen J. Wright},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computer Sciences, University of Wisconsin},
  month		= {December},
  number	= {03-04},
  title		= {Some properties of regularization schemes for {MPEC}s},
  year		= 2003
}

@Article{	  raoengacottpalmkreu:2003,
  abstract	= {We develop robust methods for subset selection based on
		  the minimization of diversity measures. A Bayesian
		  framework is used to account for noise in the data and a
		  maximum a posteriori (MAP) estimation procedure leads to an
		  iterative procedure which is a regularized version of the
		  focal underdetermined system solver (FOCUSS) algorithm. The
		  convergence of the regularized FOCUSS algorithm is
		  established and it is shown that the stable fixed points of
		  the algorithm are sparse. We investigate three different
		  criteria for choosing the regularization parameter: quality
		  of fit; sparsity criterion; L-curve. The L-curve method, as
		  applied to the problem of subset selection, is found not to
		  be robust, and we propose a novel modified L-curve
		  procedure that solves this problem. Each of the regularized
		  FOCUSS algorithms is evaluated through simulation of a
		  detection problem, and the results are compared with those
		  obtained using a sequential forward selection algorithm
		  termed orthogonal matching pursuit (OMP). In each case, the
		  regularized FOCUSS algorithm is shown to be superior to the
		  OMP in noisy environments. },
  author	= {Bhaskar D. Rao and Kjersti Engan and Shane F. Cotter and
		  Jason Palmer and Kenneth Kreutz-Delgado},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= ieeetranssigproc,
  keywords	= {Diversity measures, linear inverse problems, matching
		  pursuit, regularization, sparsity, subset selection,
		  undetermined systems},
  month		= {March},
  number	= 3,
  pages		= {760--770},
  title		= {Subset selection in noise based on diversity measure
		  minimization},
  volume	= 51,
  year		= 2003
}

###Article{	  raoengacottpalmkreu:2003,
  abstract	= {We develop robust methods for subset selection based on
		  the minimization of diversity measures. A Bayesian
		  framework is used to account for noise in the data and a
		  maximum a posteriori (MAP) estimation procedure leads to an
		  iterative procedure which is a regularized version of the
		  focal underdetermined system solver (FOCUSS) algorithm. The
		  convergence of the regularized FOCUSS algorithm is
		  established and it is shown that the stable fixed points of
		  the algorithm are sparse. We investigate three different
		  criteria for choosing the regularization parameter: quality
		  of fit; sparsity criterion; L-curve. The L-curve method, as
		  applied to the problem of subset selection, is found not to
		  be robust, and we propose a novel modified L-curve
		  procedure that solves this problem. Each of the regularized
		  FOCUSS algorithms is evaluated through simulation of a
		  detection problem, and the results are compared with those
		  obtained using a sequential forward selection algorithm
		  termed orthogonal matching pursuit (OMP). In each case, the
		  regularized FOCUSS algorithm is shown to be superior to the
		  OMP in noisy environments. },
  author	= {Bhaskar D. Rao and Kjersti Engan and Shane F. Cotter and
		  Jason Palmer and Kenneth Kreutz-Delgado},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= ieeetranssigproc,
  keywords	= {Diversity measures, linear inverse problems, matching
		  pursuit, regularization, sparsity, subset selection,
		  undetermined systems},
  month		= {March},
  number	= 3,
  pages		= {760--770},
  title		= {Subset selection in noise based on diversity measure
		  minimization},
  volume	= 51,
  year		= 2003
}

###Article{	  raoengacottpalmkreu:2003,
  abstract	= {We develop robust methods for subset selection based on
		  the minimization of diversity measures. A Bayesian
		  framework is used to account for noise in the data and a
		  maximum a posteriori (MAP) estimation procedure leads to an
		  iterative procedure which is a regularized version of the
		  focal underdetermined system solver (FOCUSS) algorithm. The
		  convergence of the regularized FOCUSS algorithm is
		  established and it is shown that the stable fixed points of
		  the algorithm are sparse. We investigate three different
		  criteria for choosing the regularization parameter: quality
		  of fit; sparsity criterion; L-curve. The L-curve method, as
		  applied to the problem of subset selection, is found not to
		  be robust, and we propose a novel modified L-curve
		  procedure that solves this problem. Each of the regularized
		  FOCUSS algorithms is evaluated through simulation of a
		  detection problem, and the results are compared with those
		  obtained using a sequential forward selection algorithm
		  termed orthogonal matching pursuit (OMP). In each case, the
		  regularized FOCUSS algorithm is shown to be superior to the
		  OMP in noisy environments. },
  author	= {Bhaskar D. Rao and Kjersti Engan and Shane F. Cotter and
		  Jason Palmer and Kenneth Kreutz-Delgado},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= ieeetranssigproc,
  keywords	= {Diversity measures, linear inverse problems, matching
		  pursuit, regularization, sparsity, subset selection,
		  undetermined systems},
  month		= {March},
  number	= 3,
  pages		= {760--770},
  title		= {Subset selection in noise based on diversity measure
		  minimization},
  volume	= 51,
  year		= 2003
}

@InProceedings{	  raokreu:1998,
  abstract	= {We consider procedures to enhance the reliability of basis
		  selection procedures with particular attention being given
		  to methods based on minimizing diversity measures. To deal
		  with noise in the data, basis selection procedures based on
		  a Bayesian framework are considered. An algorithm based on
		  the MAP estimation procedure is developed which leads to a
		  regularized version of the FOCUSS algorithm. Another
		  approach considered is to select basis vectors over
		  multiple measurement vectors thereby achieving an averaging
		  effect and enhancing the reliability. New diversity
		  measures are presented for this purpose, and algorithms are
		  derived for minimizing them },
  address	= {Pacific Grove, CA, USA},
  author	= {Bhaskar D. Rao and Kenneth Kreutz-Delgado},
  booktitle	= {Conference Record of Thirty-Second Asilomar Conference on
		  Signals, Systems and Computers},
  date-modified	= {2007-07-18 14:59:32 -0700},
  month		= {November},
  pages		= {752--756},
  publisher	= {IEEE},
  title		= {Basis selection in the presence of noise},
  volume	= 1,
  year		= 1998
}

###InProceedings{ raokreu:1998,
  abstract	= {We consider procedures to enhance the reliability of basis
		  selection procedures with particular attention being given
		  to methods based on minimizing diversity measures. To deal
		  with noise in the data, basis selection procedures based on
		  a Bayesian framework are considered. An algorithm based on
		  the MAP estimation procedure is developed which leads to a
		  regularized version of the FOCUSS algorithm. Another
		  approach considered is to select basis vectors over
		  multiple measurement vectors thereby achieving an averaging
		  effect and enhancing the reliability. New diversity
		  measures are presented for this purpose, and algorithms are
		  derived for minimizing them },
  address	= {Pacific Grove, CA, USA},
  author	= {Bhaskar D. Rao and Kenneth Kreutz-Delgado},
  booktitle	= {Conference Record of Thirty-Second Asilomar Conference on
		  Signals, Systems and Computers},
  date-modified	= {2007-07-18 14:59:32 -0700},
  month		= {November},
  pages		= {752--756},
  publisher	= {IEEE},
  title		= {Basis selection in the presence of noise},
  volume	= 1,
  year		= 1998
}

###InProceedings{ raokreu:1998,
  abstract	= {We consider procedures to enhance the reliability of basis
		  selection procedures with particular attention being given
		  to methods based on minimizing diversity measures. To deal
		  with noise in the data, basis selection procedures based on
		  a Bayesian framework are considered. An algorithm based on
		  the MAP estimation procedure is developed which leads to a
		  regularized version of the FOCUSS algorithm. Another
		  approach considered is to select basis vectors over
		  multiple measurement vectors thereby achieving an averaging
		  effect and enhancing the reliability. New diversity
		  measures are presented for this purpose, and algorithms are
		  derived for minimizing them },
  address	= {Pacific Grove, CA, USA},
  author	= {Bhaskar D. Rao and Kenneth Kreutz-Delgado},
  booktitle	= {Conference Record of Thirty-Second Asilomar Conference on
		  Signals, Systems and Computers},
  date-modified	= {2007-07-18 14:59:32 -0700},
  month		= {November},
  pages		= {752--756},
  publisher	= {IEEE},
  title		= {Basis selection in the presence of noise},
  volume	= 1,
  year		= 1998
}

@InProceedings{	  rechxuhass:2008,
  author	= {Benjamin Recht and Weiyu Xu and Babak Hassibi},
  title		= {Necessary and sufficient conditions for success of the
		  nuclear norm heuristic for rank minimization},
  booktitle	= {47th IEEE Conference on Decision and Control},
  pages		= {3065--3070},
  publisher	= {IEEE},
  month		= {December},
  year		= 2008
}

###InProceedings{ rechxuhass:2008,
  author	= {Benjamin Recht and Weiyu Xu and Babak Hassibi},
  title		= {Necessary and sufficient conditions for success of the
		  nuclear norm heuristic for rank minimization},
  booktitle	= {47th IEEE Conference on Decision and Control},
  pages		= {3065--3070},
  publisher	= {IEEE},
  month		= {December},
  year		= 2008
}

###InProceedings{ rechxuhass:2008,
  author	= {Benjamin Recht and Weiyu Xu and Babak Hassibi},
  title		= {Necessary and sufficient conditions for success of the
		  nuclear norm heuristic for rank minimization},
  booktitle	= {47th IEEE Conference on Decision and Control},
  pages		= {3065--3070},
  publisher	= {IEEE},
  month		= {December},
  year		= 2008
}

@Book{		  rene:2001,
  address	= {Philadelphia},
  author	= {J. Renegar},
  date-modified	= {2007-07-18 14:59:31 -0700},
  publisher	= siampub,
  series	= {MPS/SIAM Series on Optimization},
  title		= {A Mathematical View of Interior-Point Methods in Convex
		  Optimization},
  year		= 2001
}

###Book{	  rene:2001,
  address	= {Philadelphia},
  author	= {J. Renegar},
  date-modified	= {2007-07-18 14:59:31 -0700},
  publisher	= siampub,
  series	= {MPS/SIAM Series on Optimization},
  title		= {A Mathematical View of Interior-Point Methods in Convex
		  Optimization},
  year		= 2001
}

###Book{	  rene:2001,
  address	= {Philadelphia},
  author	= {J. Renegar},
  date-modified	= {2007-07-18 14:59:31 -0700},
  publisher	= siampub,
  series	= {MPS/SIAM Series on Optimization},
  title		= {A Mathematical View of Interior-Point Methods in Convex
		  Optimization},
  year		= 2001
}

@Misc{		  rfp07,
  author	= {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
  title		= {Guaranteed minimum-rank solutions of linear matrix
		  equations via nuclear norm minimization},
  url		= {http://arxiv.org/abs/0706.4138},
  howpublished	= {arXiv 0706.4138},
  month		= {June},
  year		= 2007
}

###Misc{	  rfp07,
  author	= {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
  title		= {Guaranteed minimum-rank solutions of linear matrix
		  equations via nuclear norm minimization},
  url		= {http://arxiv.org/abs/0706.4138},
  howpublished	= {arXiv 0706.4138},
  month		= {June},
  year		= 2007
}

###Misc{	  rfp07,
  author	= {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
  title		= {Guaranteed minimum-rank solutions of linear matrix
		  equations via nuclear norm minimization},
  url		= {http://arxiv.org/abs/0706.4138},
  howpublished	= {arXiv 0706.4138},
  month		= {June},
  year		= 2007
}

@Article{	  rfp:2010,
  author	= {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
  title		= {Guaranteed minimum-rank solutions of linear matrix
		  equations via nuclear norm minimization},
  journal	= siamrev,
  volume	= 52,
  number	= 3,
  pages		= {471-501},
  year		= 2010
}

###Article{	  rfp:2010,
  title		= {Guaranteed Minimum-Rank Solutions of Linear Matrix
		  Equations via Nuclear Norm Minimization},
  year		= 2010,
  doi		= {DOI:10.1137/070697835},
  journal	= siamreview,
  volume	= 52,
  number	= 3,
  pages		= {471-501},
  author	= {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
  keywords	= {rank; convex optimization; matrix norms; random matrices;
		  compressed sensing; semidefinite programming; 90C25; 90C59;
		  15A52; },
  url		= {http://dx.doi.org/doi/10.1137/070697835}
}

###Article{	  rfp:2010,
  title		= {Guaranteed Minimum-Rank Solutions of Linear Matrix
		  Equations via Nuclear Norm Minimization},
  year		= 2010,
  doi		= {DOI:10.1137/070697835},
  journal	= siamreview,
  volume	= 52,
  number	= 3,
  pages		= {471-501},
  author	= {Benjamin Recht and Maryam Fazel and Pablo A. Parrilo},
  keywords	= {rank; convex optimization; matrix norms; random matrices;
		  compressed sensing; semidefinite programming; 90C25; 90C59;
		  15A52; },
  url		= {http://dx.doi.org/doi/10.1137/070697835}
}

@Article{	  robbins1951stochastic,
  title		= {{A stochastic approximation method}},
  author	= {Robbins, H. and Monro, S.},
  journal	= {The Annals of Mathematical Statistics},
  pages		= {400--407},
  year		= 1951
}

@Article{	  robi:1971,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= numermath,
  pages		= {341-347},
  title		= {Extension of {N}ewton's {M}ethod to nonlinear functions
		  with values in a cone},
  volume	= 19,
  year		= 1971
}

###Article{	  robi:1971,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= numermath,
  pages		= {341-347},
  title		= {Extension of {N}ewton's {M}ethod to nonlinear functions
		  with values in a cone},
  volume	= 19,
  year		= 1971
}

###Article{	  robi:1971,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= numermath,
  pages		= {341-347},
  title		= {Extension of {N}ewton's {M}ethod to nonlinear functions
		  with values in a cone},
  volume	= 19,
  year		= 1971
}

@Article{	  robi:1972,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprog,
  pages		= {145-156},
  title		= {A Quadratically-Convergent Algorithm for General Nonlinear
		  Programming Problems},
  volume	= 3,
  year		= 1972
}

###Article{	  robi:1972,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprog,
  pages		= {145-156},
  title		= {A Quadratically-Convergent Algorithm for General Nonlinear
		  Programming Problems},
  volume	= 3,
  year		= 1972
}

###Article{	  robi:1972,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:31 -0700},
  journal	= mathprog,
  pages		= {145-156},
  title		= {A Quadratically-Convergent Algorithm for General Nonlinear
		  Programming Problems},
  volume	= 3,
  year		= 1972
}

@Article{	  robi:1974,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {1-16},
  title		= {Perturbed {K}uhn-{T}ucker Points and Rates of Convergence
		  for a Class of Nonlinear-Programming Algorithms},
  volume	= 7,
  year		= 1974
}

###Article{	  robi:1974,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {1-16},
  title		= {Perturbed {K}uhn-{T}ucker Points and Rates of Convergence
		  for a Class of Nonlinear-Programming Algorithms},
  volume	= 7,
  year		= 1974
}

###Article{	  robi:1974,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprog,
  pages		= {1-16},
  title		= {Perturbed {K}uhn-{T}ucker Points and Rates of Convergence
		  for a Class of Nonlinear-Programming Algorithms},
  volume	= 7,
  year		= 1974
}

@Article{	  robi:1984,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {217-230},
  title		= {Local structure of feasible sets in nonlinear programming,
		  Part {II}: Nondegeneracy},
  volume	= 22,
  year		= 1984
}

###Article{	  robi:1984,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {217-230},
  title		= {Local structure of feasible sets in nonlinear programming,
		  Part {II}: Nondegeneracy},
  volume	= 22,
  year		= 1984
}

###Article{	  robi:1984,
  author	= {Stephen M. Robinson},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathprogstudy,
  pages		= {217-230},
  title		= {Local structure of feasible sets in nonlinear programming,
		  Part {II}: Nondegeneracy},
  volume	= 22,
  year		= 1984
}

@PhDThesis{	  robi:2007,
  abstract	= {Nonlinearly constrained optimization problems may be
		  solved by minimizing a se- quence of simpler subproblems
		  based on the properties of a so-called \emph{merit
		  function} that balances the (usually) conflicting aims of
		  reducing the ob jective function and satisfying the
		  constraints. Sometimes this merit function is minimized
		  directly as an unconstrained function, in which case
		  convergence is achieved by adjusting the relative weighting
		  of the ob jective and constraints between subproblems.
		  Alter- natively, some model of the merit function is
		  minimized sub ject to simple bounds and/or linearizations
		  of the constraints. In this case, the merit function drives
		  the algorithm by assessing the ``quality'' of points
		  generated by the subproblem. A new primal-dual augmented
		  Lagrangian merit function is proposed that may be minimized
		  with respect to both the primal \emph{and} dual variables.
		  A benefit of this approach is that each subproblem may be
		  regularized by imposing explicit bounds on the dual
		  variables. Two primal-dual variants of classical primal
		  methods are given: a primal-dual bound constrained
		  Lagrangian (pdBCL) method and a primal-dual l1 linearly
		  constrained Lagrangian (pd$\ell1$-LCL) method. },
  author	= {D. P. Robinson},
  date-added	= {2007-10-13 18:02:27 -0700},
  date-modified	= {2007-10-13 18:04:05 -0700},
  local-url	= {file://localhost/Users/mpf/papers/Robinson07.pdf},
  school	= {University of California, San Diego},
  title		= {Primal-dual methods for nonlinear optimization},
  year		= 2007
}

###PhDThesis{	  robi:2007,
  abstract	= {Nonlinearly constrained optimization problems may be
		  solved by minimizing a se- quence of simpler subproblems
		  based on the properties of a so-called \emph{merit
		  function} that balances the (usually) conflicting aims of
		  reducing the ob jective function and satisfying the
		  constraints. Sometimes this merit function is minimized
		  directly as an unconstrained function, in which case
		  convergence is achieved by adjusting the relative weighting
		  of the ob jective and constraints between subproblems.
		  Alter- natively, some model of the merit function is
		  minimized sub ject to simple bounds and/or linearizations
		  of the constraints. In this case, the merit function drives
		  the algorithm by assessing the ``quality'' of points
		  generated by the subproblem. A new primal-dual augmented
		  Lagrangian merit function is proposed that may be minimized
		  with respect to both the primal \emph{and} dual variables.
		  A benefit of this approach is that each subproblem may be
		  regularized by imposing explicit bounds on the dual
		  variables. Two primal-dual variants of classical primal
		  methods are given: a primal-dual bound constrained
		  Lagrangian (pdBCL) method and a primal-dual l1 linearly
		  constrained Lagrangian (pd$\ell1$-LCL) method. },
  author	= {D. P. Robinson},
  date-added	= {2007-10-13 18:02:27 -0700},
  date-modified	= {2007-10-13 18:04:05 -0700},
  local-url	= {file://localhost/Users/mpf/papers/Robinson07.pdf},
  school	= {University of California, San Diego},
  title		= {Primal-dual methods for nonlinear optimization},
  year		= 2007
}

###PhDThesis{	  robi:2007,
  abstract	= {Nonlinearly constrained optimization problems may be
		  solved by minimizing a se- quence of simpler subproblems
		  based on the properties of a so-called \emph{merit
		  function} that balances the (usually) conflicting aims of
		  reducing the ob jective function and satisfying the
		  constraints. Sometimes this merit function is minimized
		  directly as an unconstrained function, in which case
		  convergence is achieved by adjusting the relative weighting
		  of the ob jective and constraints between subproblems.
		  Alter- natively, some model of the merit function is
		  minimized sub ject to simple bounds and/or linearizations
		  of the constraints. In this case, the merit function drives
		  the algorithm by assessing the ``quality'' of points
		  generated by the subproblem. A new primal-dual augmented
		  Lagrangian merit function is proposed that may be minimized
		  with respect to both the primal \emph{and} dual variables.
		  A benefit of this approach is that each subproblem may be
		  regularized by imposing explicit bounds on the dual
		  variables. Two primal-dual variants of classical primal
		  methods are given: a primal-dual bound constrained
		  Lagrangian (pdBCL) method and a primal-dual l1 linearly
		  constrained Lagrangian (pd$\ell1$-LCL) method. },
  author	= {D. P. Robinson},
  date-added	= {2007-10-13 18:02:27 -0700},
  date-modified	= {2007-10-13 18:04:05 -0700},
  local-url	= {file://localhost/Users/mpf/papers/Robinson07.pdf},
  school	= {University of California, San Diego},
  title		= {Primal-dual methods for nonlinear optimization},
  year		= 2007
}

@PhDThesis{	  robinson:2007,
  author	= {Daniel Robinson},
  title		= {Primal Dual Methods for Nonlinear Optimization},
  school	= {University of California, San Diego},
  year		= 2007,
  month		= {September}
}

###PhDThesis{	  robinson:2007,
  author	= {Daniel Robinson},
  title		= {Primal Dual Methods for Nonlinear Optimization},
  school	= {University of California, San Diego},
  year		= 2007,
  month		= {September}
}

@Book{		  roc70,
  address	= {Princeton},
  author	= {R. T. Rockafellar},
  publisher	= {Princeton University Press},
  title		= {Convex Analysis},
  year		= 1970
}

###Book{	  roc70,
  address	= {Princeton},
  author	= {R. T. Rockafellar},
  publisher	= {Princeton University Press},
  title		= {Convex Analysis},
  year		= 1970
}

###Book{	  roc70,
  address	= {Princeton},
  author	= {R. T. Rockafellar},
  publisher	= {Princeton University Press},
  title		= {Convex Analysis},
  year		= 1970
}

@Book{		  roc74,
  author	= "Rockafellar. R. T.",
  title		= "Conjugate Duality and Optimization",
  publisher	= "Society for Industrial and Applied Mathematics",
  year		= 1974,
  address	= "Philadelphia"
}

###Book{	  roc74,
  author	= "Rockafellar. R. T.",
  title		= "Conjugate Duality and Optimization",
  publisher	= "Society for Industrial and Applied Mathematics",
  year		= 1974,
  address	= "Philadelphia"
}

###Book{	  roc74,
  author	= "Rockafellar. R. T.",
  title		= "Conjugate Duality and Optimization",
  publisher	= "Society for Industrial and Applied Mathematics",
  year		= 1974,
  address	= "Philadelphia"
}

@Article{	  rockafellar-1976,
  author	= {R. T. Rockafellar},
  title		= {Augmented {L}agrangians and applications of the Proximal
		  Point Algorithm in convex programming},
  journal	= {Mathematics of Operations Research},
  volume	= {1},
  number	= {2},
  pages		= {97--116},
  year		= {1976}
}

###Article{	  rockafellar-1976,
  author	= {R. T. Rockafellar},
  title		= {Augmented {L}agrangians and applications of the Proximal
		  Point Algorithm in convex programming},
  journal	= {Mathematics of Operations Research},
  volume	= {1},
  number	= {2},
  pages		= {97--116},
  year		= {1976}
}

@Article{	  rockafellar:1990,
  author	= {R. T. Rockafellar},
  title		= {Computational schemes for large-scale problems in extended
		  linear-quadratic programming},
  journal	= mathprog,
  year		= 1990,
  pages		= {447--474}
}

###Article{	  rockafellar:1990,
  author	= {R. T. Rockafellar},
  title		= {Computational schemes for large-scale problems in extended
		  linear-quadratic programming},
  journal	= mathprog,
  year		= 1990,
  pages		= {447--474}
}

###Article{	  rockafellar:1990,
  author	= {R. T. Rockafellar},
  title		= {Computational schemes for large-scale problems in extended
		  linear-quadratic programming},
  journal	= mathprog,
  year		= 1990,
  pages		= {447--474}
}

@Article{	  rockafellar:1993,
  title		= {Lagrange Multipliers and Optimality},
  author	= {Rockafellar, R. T.},
  journal	= {SIAM Review},
  volume	= 35,
  number	= 2,
  pages		= {pp. 183-238},
  url		= {http://www.jstor.org/stable/2133143},
  issn		= 00361445,
  abstract	= {Lagrange multipliers used to be viewed as auxiliary
		  variables introduced in a problem of constrained
		  minimization in order to write first-order optimality
		  conditions formally as a system of equations. Modern
		  applications, with their emphasis on numerical methods and
		  more complicated side conditions than equations, have
		  demanded deeper understanding of the concept and how it
		  fits into a larger theoretical picture. A major line of
		  research has been the nonsmooth geometry of one-sided
		  tangent and normal vectors to the set of points satisfying
		  the given constraints. Another has been the game-theoretic
		  role of multiplier vectors as solutions to a dual problem.
		  Interpretations as generalized derivatives of the optimal
		  value with respect to problem parameters have also been
		  explored. Lagrange multipliers are now being seen as
		  arising from a general rule for the subdifferentiation of a
		  nonsmooth objective function which allows black-and-white
		  constraints to be replaced by penalty expressions. This
		  paper traces such themes in the current theory of Lagrange
		  multipliers, providing along the way a free-standing
		  exposition of basic nonsmooth analysis as motivated by and
		  applied to this subject.},
  year		= 1993
}

###Article{	  rockafellar:1993,
  title		= {Lagrange Multipliers and Optimality},
  author	= {Rockafellar, R. T.},
  journal	= siamreview,
  volume	= 35,
  number	= 2,
  pages		= {pp. 183-238},
  url		= {http://www.jstor.org/stable/2133143},
  abstract	= {Lagrange multipliers used to be viewed as auxiliary
		  variables introduced in a problem of constrained
		  minimization in order to write first-order optimality
		  conditions formally as a system of equations. Modern
		  applications, with their emphasis on numerical methods and
		  more complicated side conditions than equations, have
		  demanded deeper understanding of the concept and how it
		  fits into a larger theoretical picture. A major line of
		  research has been the nonsmooth geometry of one-sided
		  tangent and normal vectors to the set of points satisfying
		  the given constraints. Another has been the game-theoretic
		  role of multiplier vectors as solutions to a dual problem.
		  Interpretations as generalized derivatives of the optimal
		  value with respect to problem parameters have also been
		  explored. Lagrange multipliers are now being seen as
		  arising from a general rule for the subdifferentiation of a
		  nonsmooth objective function which allows black-and-white
		  constraints to be replaced by penalty expressions. This
		  paper traces such themes in the current theory of Lagrange
		  multipliers, providing along the way a free-standing
		  exposition of basic nonsmooth analysis as motivated by and
		  applied to this subject.},
  year		= 1993
}

###Article{	  rockafellar:1993,
  title		= {Lagrange Multipliers and Optimality},
  author	= {Rockafellar, R. T.},
  journal	= siamreview,
  volume	= 35,
  number	= 2,
  pages		= {pp. 183-238},
  url		= {http://www.jstor.org/stable/2133143},
  abstract	= {Lagrange multipliers used to be viewed as auxiliary
		  variables introduced in a problem of constrained
		  minimization in order to write first-order optimality
		  conditions formally as a system of equations. Modern
		  applications, with their emphasis on numerical methods and
		  more complicated side conditions than equations, have
		  demanded deeper understanding of the concept and how it
		  fits into a larger theoretical picture. A major line of
		  research has been the nonsmooth geometry of one-sided
		  tangent and normal vectors to the set of points satisfying
		  the given constraints. Another has been the game-theoretic
		  role of multiplier vectors as solutions to a dual problem.
		  Interpretations as generalized derivatives of the optimal
		  value with respect to problem parameters have also been
		  explored. Lagrange multipliers are now being seen as
		  arising from a general rule for the subdifferentiation of a
		  nonsmooth objective function which allows black-and-white
		  constraints to be replaced by penalty expressions. This
		  paper traces such themes in the current theory of Lagrange
		  multipliers, providing along the way a free-standing
		  exposition of basic nonsmooth analysis as motivated by and
		  applied to this subject.},
  year		= 1993
}

@Misc{		  romb:2005,
  author	= {J. Romberg},
  date-modified	= {2007-07-18 14:59:33 -0700},
  howpublished	= {{\sc Matlab} solver for {DS} problem,
		  \url{http://www.l1-magic.org/}},
  title		= {{l1dantzig\_pd.m}},
  year		= 2005
}

###Misc{	  romb:2005,
  author	= {J. Romberg},
  date-modified	= {2007-07-18 14:59:33 -0700},
  howpublished	= {{\sc Matlab} solver for {DS} problem,
		  \url{http://www.l1-magic.org/}},
  title		= {{l1dantzig\_pd.m}},
  year		= 2005
}

###Misc{	  romb:2005,
  author	= {J. Romberg},
  date-modified	= {2007-07-18 14:59:33 -0700},
  howpublished	= {{\sc Matlab} solver for {DS} problem,
		  \url{http://www.l1-magic.org/}},
  title		= {{l1dantzig\_pd.m}},
  year		= 2005
}

@InBook{	  rose:1963,
  author	= {J. B. Rosen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {R. L. Graves and P. Wolfe},
  publisher	= mcgrawhill,
  title		= {Recent Advances in Mathematical Programming},
  year		= 1963
}

###InBook{	  rose:1963,
  author	= {J. B. Rosen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {R. L. Graves and P. Wolfe},
  publisher	= mcgrawhill,
  title		= {Recent Advances in Mathematical Programming},
  year		= 1963
}

###InBook{	  rose:1963,
  author	= {J. B. Rosen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {R. L. Graves and P. Wolfe},
  publisher	= mcgrawhill,
  title		= {Recent Advances in Mathematical Programming},
  year		= 1963
}

@InCollection{	  rose:1963b,
  address	= {New York},
  author	= {J. B. Rosen},
  booktitle	= {Recent Advances in Mathematical Programming},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {R. L. Graves and P. Wolfe},
  pages		= {159-176},
  publisher	= mcgrawhill,
  title		= {Convex Partition Programming},
  year		= 1963
}

###InCollection{  rose:1963b,
  address	= {New York},
  author	= {J. B. Rosen},
  booktitle	= {Recent Advances in Mathematical Programming},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {R. L. Graves and P. Wolfe},
  pages		= {159-176},
  publisher	= mcgrawhill,
  title		= {Convex Partition Programming},
  year		= 1963
}

###InCollection{  rose:1963b,
  address	= {New York},
  author	= {J. B. Rosen},
  booktitle	= {Recent Advances in Mathematical Programming},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {R. L. Graves and P. Wolfe},
  pages		= {159-176},
  publisher	= mcgrawhill,
  title		= {Convex Partition Programming},
  year		= 1963
}

@TechReport{	  rose:1977,
  address	= {Minneapolis, MN},
  author	= {J. B. Rosen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computer Science Department, University of Minnesota},
  month		= {August},
  number	= {77-8},
  title		= {Two-phase algorithm for nonlinear constraint problems},
  year		= 1977
}

###TechReport{	  rose:1977,
  address	= {Minneapolis, MN},
  author	= {J. B. Rosen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computer Science Department, University of Minnesota},
  month		= {August},
  number	= {77-8},
  title		= {Two-phase algorithm for nonlinear constraint problems},
  year		= 1977
}

###TechReport{	  rose:1977,
  address	= {Minneapolis, MN},
  author	= {J. B. Rosen},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Computer Science Department, University of Minnesota},
  month		= {August},
  number	= {77-8},
  title		= {Two-phase algorithm for nonlinear constraint problems},
  year		= 1977
}

@InProceedings{	  rose:1978,
  address	= {New York},
  author	= {J. B. Rosen},
  booktitle	= {Nonlinear Programming 3},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {O. Mangasarian and R. Meyer and S. Robinson},
  pages		= {97--124},
  publisher	= academic,
  title		= {{Two-phase} algorithm for nonlinear constraint problems},
  year		= 1978
}

###InProceedings{ rose:1978,
  address	= {New York},
  author	= {J. B. Rosen},
  booktitle	= {Nonlinear Programming 3},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {O. Mangasarian and R. Meyer and S. Robinson},
  pages		= {97--124},
  publisher	= academic,
  title		= {{Two-phase} algorithm for nonlinear constraint problems},
  year		= 1978
}

###InProceedings{ rose:1978,
  address	= {New York},
  author	= {J. B. Rosen},
  booktitle	= {Nonlinear Programming 3},
  date-modified	= {2007-07-18 14:59:34 -0700},
  editor	= {O. Mangasarian and R. Meyer and S. Robinson},
  pages		= {97--124},
  publisher	= academic,
  title		= {{Two-phase} algorithm for nonlinear constraint problems},
  year		= 1978
}

@InCollection{	  rosekreu:1972,
  address	= {London},
  author	= {J. B. Rosen and J. Kreuser},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {F. A. Lootsma},
  pages		= {297--300},
  publisher	= academic,
  title		= {A Gradient Projection Algorithm for Non-Linear
		  Constraints},
  year		= 1972
}

###InCollection{  rosekreu:1972,
  address	= {London},
  author	= {J. B. Rosen and J. Kreuser},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {F. A. Lootsma},
  pages		= {297--300},
  publisher	= academic,
  title		= {A Gradient Projection Algorithm for Non-Linear
		  Constraints},
  year		= 1972
}

###InCollection{  rosekreu:1972,
  address	= {London},
  author	= {J. B. Rosen and J. Kreuser},
  booktitle	= {Numerical Methods for Nonlinear Optimization},
  date-modified	= {2007-07-18 14:59:31 -0700},
  editor	= {F. A. Lootsma},
  pages		= {297--300},
  publisher	= academic,
  title		= {A Gradient Projection Algorithm for Non-Linear
		  Constraints},
  year		= 1972
}

@InProceedings{	  roux2012stochastic,
  title		= {A Stochastic Gradient Method with an Exponential
		  Convergence Rate for Finite Training Sets},
  author	= {Roux, Nicolas L and Schmidt, Mark and Bach, Francis R},
  booktitle	= {Advances Neural Inform. Processing Systems},
  pages		= {2663--2671},
  year		= 2012
}

###InProceedings{ roux2012stochastic,
  title		= {A Stochastic Gradient Method with an Exponential
		  Convergence Rate for Finite Training Sets},
  author	= {Roux, Nicolas L and Schmidt, Mark and Bach, Francis R},
  booktitle	= {Advances in Neural Information Processing Systems},
  pages		= {2663--2671},
  year		= 2012
}

@Book{		  roydenf:2010,
  author	= {H. L. Royden and P. M. Fitzpatrick},
  title		= {Real analysis},
  publisher	= {Prentice Hall},
  year		= 2010,
  month		= {Boston},
  note		= {4th edition}
}

###Book{	  roydenf:2010,
  author	= {H. L. Royden and P. M. Fitzpatrick},
  title		= {Real analysis},
  publisher	= {Prentice Hall},
  year		= 2010,
  month		= {Boston},
  note		= {4th edition}
}

###Book{	  roydenf:2010,
  author	= {H. L. Royden and P. M. Fitzpatrick},
  title		= {Real analysis},
  publisher	= {Prentice Hall},
  year		= 2010,
  month		= {Boston},
  note		= {4th edition}
}

@Article{	  rrt94,
  author	= "Ravi, S. S. and Rosenkrantz, D. J. and Tayi, G. K.",
  title		= "Heuristic and special case algorithms for dispersion
		  problems",
  journal	= "Oper. Res.",
  year		= 1994,
  volume	= 42,
  page		= {299--310}
}

###Article{	  rrt94,
  author	= "Ravi, S. S. and Rosenkrantz, D. J. and Tayi, G. K.",
  title		= "Heuristic and special case algorithms for dispersion
		  problems",
  journal	= "Oper. Res.",
  year		= 1994,
  volume	= 42,
  page		= {299--310}
}

###Article{	  rrt94,
  author	= "Ravi, S. S. and Rosenkrantz, D. J. and Tayi, G. K.",
  title		= "Heuristic and special case algorithms for dispersion
		  problems",
  journal	= "Oper. Res.",
  year		= 1994,
  volume	= 42,
  page		= {299--310}
}

@Article{	  rsmb04,
  author	= "Rohl, C. A. and Strauss, C. E. M. and Misura, K. and
		  Baker, D.",
  title		= "Protein structure prediction using Rosetta",
  journal	= "Methods Enzym.",
  year		= 2004,
  volume	= 383,
  page		= {66--93}
}

###Article{	  rsmb04,
  author	= "Rohl, C. A. and Strauss, C. E. M. and Misura, K. and
		  Baker, D.",
  title		= "Protein structure prediction using Rosetta",
  journal	= "Methods Enzym.",
  year		= 2004,
  volume	= 383,
  page		= {66--93}
}

###Article{	  rsmb04,
  author	= "Rohl, C. A. and Strauss, C. E. M. and Misura, K. and
		  Baker, D.",
  title		= "Protein structure prediction using Rosetta",
  journal	= "Methods Enzym.",
  year		= 2004,
  volume	= 383,
  page		= {66--93}
}

@Book{		  rtrw:1998,
  author	= {R. T. Rockafellar and R. J. B. Wets},
  publisher	= {Springer},
  title		= {Variational Analysis},
  volume	= 317,
  year		= 1998,
  note		= {Corrected 3rd printing}
}

###Book{	  rtrw:1998,
  author	= {R. T. Rockafellar and R. J. B. Wets},
  publisher	= {Springer},
  title		= {Variational Analysis},
  volume	= 317,
  year		= 1998,
  note		= {3rd printing}
}

###Book{	  rtrw:1998,
  author	= {R. T. Rockafellar and R. J. B. Wets},
  publisher	= {Springer},
  title		= {Variational Analysis},
  volume	= 317,
  year		= 1998,
  note		= {3rd printing}
}

@Article{	  rudioshefate:1992,
  author	= {L. Rudin and S. Osher and E. Fatemi},
  journal	= {Physica D},
  pages		= {259--268},
  title		= {Nonlinear total variation based noise removal algorithms},
  volume	= 60,
  year		= 1992
}

###Article{	  rudioshefate:1992,
  author	= {L. Rudin and S. Osher and E. Fatemi},
  journal	= {Physica D},
  pages		= {259--268},
  title		= {Nonlinear total variation based noise removal algorithms},
  volume	= 60,
  year		= 1992
}

###Article{	  rudioshefate:1992,
  author	= {L. Rudin and S. Osher and E. Fatemi},
  journal	= {Physica D},
  pages		= {259--268},
  title		= {Nonlinear total variation based noise removal algorithms},
  volume	= 60,
  year		= 1992
}

@Misc{		  rwt,
  author	= {R. Baraniuk and H. Choi and F. Fernandes and B. Hendricks
		  and R. Neelamani and V. Ribeiro and J. Romberg and R.
		  Gopinath and H.-T Guo and M. Lang and J. E. Odegard and D.
		  Wei},
  date-added	= {2007-09-18 22:09:54 -0700},
  date-modified	= {2007-09-18 22:09:54 -0700},
  howpublished	= {\url{http://www.dsp.rice.edu/software/rwt.shtml}},
  title		= {{Rice Wavelet Toolbox}},
  year		= 1993
}

###Misc{	  rwt,
  author	= {R. Baraniuk and H. Choi and F. Fernandes and B. Hendricks
		  and R. Neelamani and V. Ribeiro and J. Romberg and R.
		  Gopinath and H.-T Guo and M. Lang and J. E. Odegard and D.
		  Wei},
  date-added	= {2007-09-18 22:09:54 -0700},
  date-modified	= {2007-09-18 22:09:54 -0700},
  howpublished	= {\url{http://www.dsp.rice.edu/software/rwt.shtml}},
  title		= {{Rice Wavelet Toolbox}},
  year		= 1993
}

###Misc{	  rwt,
  author	= {R. Baraniuk and H. Choi and F. Fernandes and B. Hendricks
		  and R. Neelamani and V. Ribeiro and J. Romberg and R.
		  Gopinath and H.-T Guo and M. Lang and J. E. Odegard and D.
		  Wei},
  date-added	= {2007-09-18 22:09:54 -0700},
  date-modified	= {2007-09-18 22:09:54 -0700},
  howpublished	= {\url{http://www.dsp.rice.edu/software/rwt.shtml}},
  title		= {{Rice Wavelet Toolbox}},
  year		= 1993
}

@Article{	  ryanosbo:1986,
  title		= {On the solution of highly degenerate linear programmes},
  author	= {Ryan, D. M. and Osborne, M.R.},
  journal	= mathprog,
  volume	= 41,
  number	= 1,
  pages		= {385--392},
  year		= 1986
}

###Article{	  ryanosbo:1986,
  title		= {On the solution of highly degenerate linear programmes},
  author	= {Ryan, D. M. and Osborne, M.R.},
  journal	= mathprog,
  volume	= 41,
  number	= 1,
  pages		= {385--392},
  year		= 1986
}

###Article{	  ryanosbo:1986,
  title		= {On the solution of highly degenerate linear programmes},
  author	= {Ryan, D. M. and Osborne, M.R.},
  journal	= mathprog,
  volume	= 41,
  number	= 1,
  pages		= {385--392},
  year		= 1986
}

@InProceedings{	  sangbuc:2000,
  author	= {E. Sang and S. Buchholz},
  title		= {Introduction to the {CoNLL-2000 Shared Task: Chunking}},
  booktitle	= {Proceedings of Conference on Natural Language Learning},
  pages		= {127--132},
  year		= 2000
}

###InProceedings{ sangbuc:2000,
  author	= {E. Sang and S. Buchholz},
  title		= {Introduction to the {CoNLL-2000 Shared Task: Chunking}},
  booktitle	= {Proceedings of Conference on Natural Language Learning},
  pages		= {127--132},
  year		= 2000
}

###InProceedings{ sangbuc:2000,
  author	= {E. Sang and S. Buchholz},
  title		= {Introduction to the {CoNLL-2000 Shared Task: Chunking}},
  booktitle	= {Proceedings of Conference on Natural Language Learning},
  pages		= {127--132},
  year		= 2000
}

@Article{	  sardy2004c,
  author	= "Sardy, S. and Tseng, P.",
  title		= "On the statistical analysis of smoothing by maximizing
		  dirty Markov random field posterior distributions",
  journal	= "J. Amer. Statist. Assoc.",
  year		= 2004,
  volume	= 99,
  page		= {191--204}
}

###Article{	  sardy2004c,
  author	= "Sardy, S. and Tseng, P.",
  title		= "On the statistical analysis of smoothing by maximizing
		  dirty Markov random field posterior distributions",
  journal	= "J. Amer. Statist. Assoc.",
  year		= 2004,
  volume	= 99,
  page		= {191--204}
}

###Article{	  sardy2004c,
  author	= "Sardy, S. and Tseng, P.",
  title		= "On the statistical analysis of smoothing by maximizing
		  dirty Markov random field posterior distributions",
  journal	= "J. Amer. Statist. Assoc.",
  year		= 2004,
  volume	= 99,
  page		= {191--204}
}

@Article{	  sas02,
  author	= "Sagastizabal, C. A. and Solodov, M. V.",
  title		= "Parallel variable distribution for constrained
		  optimization",
  journal	= "Computational Optimization and Applications",
  year		= 2002,
  volume	= 22,
  page		= {111--131}
}

###Article{	  sas02,
  author	= "Sagastizabal, C. A. and Solodov, M. V.",
  title		= "Parallel variable distribution for constrained
		  optimization",
  journal	= "Computational Optimization and Applications",
  year		= 2002,
  volume	= 22,
  page		= {111--131}
}

###Article{	  sas02,
  author	= "Sagastizabal, C. A. and Solodov, M. V.",
  title		= "Parallel variable distribution for constrained
		  optimization",
  journal	= "Computational Optimization and Applications",
  year		= 2002,
  volume	= 22,
  page		= {111--131}
}

@Article{	  sat04,
  author	= "Sardy, S. and Antoniadis, A. and Tseng, P.",
  title		= "Automatic smoothing with wavelets for a wide class of
		  distributions",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2004,
  volume	= 13,
  page		= {399--421}
}

###Article{	  sat04,
  author	= "Sardy, S. and Antoniadis, A. and Tseng, P.",
  title		= "Automatic smoothing with wavelets for a wide class of
		  distributions",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2004,
  volume	= 13,
  page		= {399--421}
}

###Article{	  sat04,
  author	= "Sardy, S. and Antoniadis, A. and Tseng, P.",
  title		= "Automatic smoothing with wavelets for a wide class of
		  distributions",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2004,
  volume	= 13,
  page		= {399--421}
}

@Article{	  sat04a,
  author	= {S. Sardy and P. Tseng},
  journal	= {J. Amer. Statist. Assoc.},
  pages		= {191-204},
  title		= {On the statistical analysis of smoothing by maximizing
		  dirty Markov random field posterior distributions},
  volume	= 99,
  year		= 2004
}

###Article{	  sat04a,
  author	= {S. Sardy and P. Tseng},
  journal	= {J. Amer. Statist. Assoc.},
  pages		= {191-204},
  title		= {On the statistical analysis of smoothing by maximizing
		  dirty Markov random field posterior distributions},
  volume	= 99,
  year		= 2004
}

###Article{	  sat04a,
  author	= {S. Sardy and P. Tseng},
  journal	= {J. Amer. Statist. Assoc.},
  pages		= {191-204},
  title		= {On the statistical analysis of smoothing by maximizing
		  dirty Markov random field posterior distributions},
  volume	= 99,
  year		= 2004
}

@Article{	  sat04b,
  author	= "Sardy, S. and Tseng, P.",
  title		= "{AMlet}, {RAMlet}, and {GAMlet}: automatic nonlinear
		  fitting of additive models, robust and generalized, with
		  wavelets",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2004,
  volume	= 13,
  page		= {283--309}
}

###Article{	  sat04b,
  author	= "Sardy, S. and Tseng, P.",
  title		= "{AMlet}, {RAMlet}, and {GAMlet}: automatic nonlinear
		  fitting of additive models, robust and generalized, with
		  wavelets",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2004,
  volume	= 13,
  page		= {283--309}
}

###Article{	  sat04b,
  author	= "Sardy, S. and Tseng, P.",
  title		= "{AMlet}, {RAMlet}, and {GAMlet}: automatic nonlinear
		  fitting of additive models, robust and generalized, with
		  wavelets",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2004,
  volume	= 13,
  page		= {283--309}
}

@InCollection{	  saun:1996,
  address	= {Philadelphia},
  author	= {M. A. Saunders},
  booktitle	= {Linear and Nonlinear Conjugate Gradient-Related Methods},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {L. Adams and J. L. Nazareth},
  pages		= {92--100},
  publisher	= siampub,
  title		= {Cholesky-based methods for sparse least squares: {T}he
		  benefits of regularization},
  year		= 1996
}

###InCollection{  saun:1996,
  address	= {Philadelphia},
  author	= {M. A. Saunders},
  booktitle	= {Linear and Nonlinear Conjugate Gradient-Related Methods},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {L. Adams and J. L. Nazareth},
  pages		= {92--100},
  publisher	= siampub,
  title		= {Cholesky-based methods for sparse least squares: {T}he
		  benefits of regularization},
  year		= 1996
}

###InCollection{  saun:1996,
  address	= {Philadelphia},
  author	= {M. A. Saunders},
  booktitle	= {Linear and Nonlinear Conjugate Gradient-Related Methods},
  date-modified	= {2007-07-18 14:59:32 -0700},
  editor	= {L. Adams and J. L. Nazareth},
  pages		= {92--100},
  publisher	= siampub,
  title		= {Cholesky-based methods for sparse least squares: {T}he
		  benefits of regularization},
  year		= 1996
}

@Misc{		  saun:2010,
  author	= {Michael A. Saunders},
  howpublished	= {\url{http://www.stanford.edu/group/SOL/software/pdco.html}},
  title		= {{PDCO: Primal-Dual interior method for Convex
		  Objectives}},
  month		= {April},
  year		= 2010
}

###Misc{	  saun:2010,
  author	= {Michael A. Saunders},
  howpublished	= {\url{http://www.stanford.edu/group/SOL/software/pdco.html}},
  title		= {{PDCO: Primal-Dual interior method for Convex
		  Objectives}},
  month		= {April},
  year		= 2010
}

###Misc{	  saun:2010,
  author	= {Michael A. Saunders},
  howpublished	= {\url{http://www.stanford.edu/group/SOL/software/pdco.html}},
  title		= {{PDCO: Primal-Dual interior method for Convex
		  Objectives}},
  month		= {April},
  year		= 2010
}

@TechReport{	  sauntoml:1996,
  address	= {Department of {EES} \& {OR}, Stanford, CA 94305},
  author	= {M. A. Saunders and J. A. Tomlin},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Stanford University},
  month		= {December},
  note		= {Also IBM Research Report RJ 10064},
  number	= {SOL Report 96-4},
  title		= {Solving regularized linear programs using barrier methods
		  and {KKT} systems},
  year		= 1996
}

###TechReport{	  sauntoml:1996,
  address	= {Department of {EES} \& {OR}, Stanford, CA 94305},
  author	= {M. A. Saunders and J. A. Tomlin},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Stanford University},
  month		= {December},
  note		= {Also IBM Research Report RJ 10064},
  number	= {SOL Report 96-4},
  title		= {Solving regularized linear programs using barrier methods
		  and {KKT} systems},
  year		= 1996
}

###TechReport{	  sauntoml:1996,
  address	= {Department of {EES} \& {OR}, Stanford, CA 94305},
  author	= {M. A. Saunders and J. A. Tomlin},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Stanford University},
  month		= {December},
  note		= {Also IBM Research Report RJ 10064},
  number	= {SOL Report 96-4},
  title		= {Solving regularized linear programs using barrier methods
		  and {KKT} systems},
  year		= 1996
}

@Article{	  sbt00,
  author	= "Sardy, S. and Bruce, A. and Tseng, P.",
  title		= "Block coordinate relaxation methods for nonparametric
		  wavelet denoising",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2000,
  volume	= 9,
  page		= {361--379}
}

###Article{	  sbt00,
  author	= "Sardy, S. and Bruce, A. and Tseng, P.",
  title		= "Block coordinate relaxation methods for nonparametric
		  wavelet denoising",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2000,
  volume	= 9,
  page		= {361--379}
}

###Article{	  sbt00,
  author	= "Sardy, S. and Bruce, A. and Tseng, P.",
  title		= "Block coordinate relaxation methods for nonparametric
		  wavelet denoising",
  journal	= "J. Comput. Graph. Stat.",
  year		= 2000,
  volume	= 9,
  page		= {361--379}
}

@Article{	  sbt01,
  author	= "Sardy, S. and Bruce, A. and Tseng, P.",
  title		= "Robust wavelet denoising",
  journal	= "IEEE Trans. Signal Proc.",
  year		= 2001,
  volume	= 49,
  page		= {1146--1152}
}

###Article{	  sbt01,
  author	= "Sardy, S. and Bruce, A. and Tseng, P.",
  title		= "Robust wavelet denoising",
  journal	= "IEEE Trans. Signal Proc.",
  year		= 2001,
  volume	= 49,
  page		= {1146--1152}
}

###Article{	  sbt01,
  author	= "Sardy, S. and Bruce, A. and Tseng, P.",
  title		= "Robust wavelet denoising",
  journal	= "IEEE Trans. Signal Proc.",
  year		= 2001,
  volume	= 49,
  page		= {1146--1152}
}

@Article{	  scheinberg2016,
  author	= "Scheinberg, Katya and Tang, Xiaocheng",
  title		= "Practical inexact proximal quasi-{Newton} method with
		  global complexity analysis",
  journal	= mathprog,
  year		= 2016,
  volume	= 160,
  number	= 1,
  pages		= "495--529",
  abstract	= "Recently several methods were proposed for sparse
		  optimization which make careful use of second-order
		  information (Hsieh et al. in Sparse inverse covariance
		  matrix estimation using quadratic approximation. In: NIPS,
		  2011; Yuan et al. in An improved GLMNET for l1-regularized
		  logistic regression and support vector machines. National
		  Taiwan University, Taipei City, 2011; Olsen et al. in
		  Newton-like methods for sparse inverse covariance
		  estimation. In: NIPS, 2012; Byrd et al. in A family of
		  second-order methods for convex l1-regularized
		  optimization. Technical report, 2012) to improve local
		  convergence rates. These methods construct a composite
		  quadratic approximation using Hessian information, optimize
		  this approximation using a first-order method, such as
		  coordinate descent and employ a line search to ensure
		  sufficient descent. Here we propose a general framework,
		  which includes slightly modified versions of existing
		  algorithms and also a new algorithm, which uses limited
		  memory BFGS Hessian approximations, and provide a novel
		  global convergence rate analysis, which covers methods that
		  solve subproblems via coordinate descent."
}

@Article{	  schescho:2000,
  author	= {Holger Scheel and Stefan Scholtes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathofor,
  month		= {February},
  number	= 1,
  title		= {Mathematical programs with complementarity constraints:
		  Stationarity, optimality, and sensitivity},
  volume	= 25,
  year		= 2000
}

###Article{	  schescho:2000,
  author	= {Holger Scheel and Stefan Scholtes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathofor,
  month		= {February},
  number	= 1,
  title		= {Mathematical programs with complementarity constraints:
		  Stationarity, optimality, and sensitivity},
  volume	= 25,
  year		= 2000
}

###Article{	  schescho:2000,
  author	= {Holger Scheel and Stefan Scholtes},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= mathofor,
  month		= {February},
  number	= 1,
  title		= {Mathematical programs with complementarity constraints:
		  Stationarity, optimality, and sensitivity},
  volume	= 25,
  year		= 2000
}

@TechReport{	  schi:2003,
  author	= {K. Schittkowski},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Mathematics, University of Bayreuth},
  title		= {{QL}: {A} {F}ortran code for convex quadratic programming
		  - {U}ser's guide},
  year		= 2003
}

###TechReport{	  schi:2003,
  author	= {K. Schittkowski},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Mathematics, University of Bayreuth},
  title		= {{QL}: {A} {F}ortran code for convex quadratic programming
		  - {U}ser's guide},
  year		= 2003
}

###TechReport{	  schi:2003,
  author	= {K. Schittkowski},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Mathematics, University of Bayreuth},
  title		= {{QL}: {A} {F}ortran code for convex quadratic programming
		  - {U}ser's guide},
  year		= 2003
}

@Article{	  schittkowski:1981,
  author	= {K. Schittkowski},
  title		= {The nonlinear programming method of {Wilson, Han, and
		  Powell} with an augmented {Lagrangian} type line search
		  function. {I.} Convergence analysis},
  journal	= numermath,
  volume	= 38,
  number	= 1,
  pages		= {83–114},
  year		= {1981/82}
}

###Article{	  schittkowski:1981,
  author	= {K. Schittkowski},
  title		= {The nonlinear programming method of {Wilson, Han, and
		  Powell} with an augmented {Lagrangian} type line search
		  function. {I.} Convergence analysis},
  journal	= numermath,
  volume	= 38,
  number	= 1,
  pages		= {83–114},
  year		= {1981/82}
}

###Article{	  schittkowski:1981,
  author	= {K. Schittkowski},
  title		= {The nonlinear programming method of {Wilson, Han, and
		  Powell} with an augmented {Lagrangian} type line search
		  function. {I.} Convergence analysis},
  journal	= numermath,
  volume	= 38,
  number	= 1,
  pages		= {83–114},
  year		= {1981/82}
}

@InProceedings{	  schmidt2009optimizing,
  author	= {M. Schmidt and E. van den Berg and M. P. Friedlander and
		  K. Murphy},
  usera		= {1 2},
  title		= {Optimizing costly functions with simple constraints: a
		  limited-memory projected quasi-{N}ewton algorithm},
  booktitle	= {Proc.\@ 12th Inter.\@ Conf.\@ Artificial Intelligence and
		  Stat.},
  location	= {Clearwater Beach, Florida},
  year		= 2009,
  pages		= {448--455},
  month		= {April}
}


@Article{	  schmidtrouxbach:2011,
  title		= {Convergence rates of inexact proximal-gradient methods for
		  convex optimization},
  author	= {Schmidt, Mark and Roux, Nicolas Le and Bach, Francis},
  journal	= {arXiv preprint arXiv:1109.2415},
  year		= 2011
}

###Article{	  schmidtrouxbach:2011,
  title		= {Convergence rates of inexact proximal-gradient methods for
		  convex optimization},
  author	= {Schmidt, Mark and Roux, Nicolas Le and Bach, Francis},
  journal	= {arXiv preprint arXiv:1109.2415},
  year		= 2011
}

###Article{	  schmidtrouxbach:2011,
  title		= {Convergence rates of inexact proximal-gradient methods for
		  convex optimization},
  author	= {Schmidt, Mark and Roux, Nicolas Le and Bach, Francis},
  journal	= {arXiv preprint arXiv:1109.2415},
  year		= 2011
}

@Article{	  scho:2001,
  author	= {S. Scholtes},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {918-936},
  title		= {Convergence properties of a regularization scheme for
		  mathematical programs with complementarity constraints},
  volume	= 11,
  year		= 2001
}

###Article{	  scho:2001,
  author	= {S. Scholtes},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {918-936},
  title		= {Convergence properties of a regularization scheme for
		  mathematical programs with complementarity constraints},
  volume	= 11,
  year		= 2001
}

###Article{	  scho:2001,
  author	= {S. Scholtes},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {918-936},
  title		= {Convergence properties of a regularization scheme for
		  mathematical programs with complementarity constraints},
  volume	= 11,
  year		= 2001
}

@Article{	  schopfer:2012,
  author	= {Schöpfer, F.},
  title		= {Exact Regularization of Polyhedral Norms},
  journal	= {SIAM Journal on Optimization},
  volume	= 22,
  number	= 4,
  pages		= {1206-1223},
  year		= 2012,
  doi		= {10.1137/11085236X},
  url		= {http://epubs.siam.org/doi/abs/10.1137/11085236X}
}

###Article{	  schopfer:2012,
  author	= {Sch\"opfer, F.},
  title		= {Exact Regularization of Polyhedral Norms},
  journal	= siamopt,
  volume	= 22,
  number	= 4,
  pages		= {1206-1223},
  year		= 2012,
  doi		= {10.1137/11085236X},
  url		= {http://epubs.siam.org/doi/abs/10.1137/11085236X}
}

###Article{	  schopfer:2012,
  author	= {Sch\"opfer, F.},
  title		= {Exact Regularization of Polyhedral Norms},
  journal	= siamopt,
  volume	= 22,
  number	= 4,
  pages		= {1206-1223},
  year		= 2012,
  doi		= {10.1137/11085236X},
  url		= {http://epubs.siam.org/doi/abs/10.1137/11085236X}
}

@Article{	  schostoh:1999,
  author	= {S. Scholtes and M. St\"ohr},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamcontrol,
  pages		= {617-652},
  title		= {Exact penalization of mathematical programs with
		  equilibrium constraints},
  volume	= 37,
  year		= 1999
}

###Article{	  schostoh:1999,
  author	= {S. Scholtes and M. St\"ohr},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamcontrol,
  pages		= {617-652},
  title		= {Exact penalization of mathematical programs with
		  equilibrium constraints},
  volume	= 37,
  year		= 1999
}

###Article{	  schostoh:1999,
  author	= {S. Scholtes and M. St\"ohr},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= siamcontrol,
  pages		= {617-652},
  title		= {Exact penalization of mathematical programs with
		  equilibrium constraints},
  volume	= 37,
  year		= 1999
}

@Article{	  schostoh:2001,
  author	= {S. Scholtes and M. St\"ohr},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathofor,
  pages		= {851-863},
  title		= {How stringent is the linear independence condition for
		  mathematical programs with equilibrium constraints?},
  volume	= 26,
  year		= 2001
}

###Article{	  schostoh:2001,
  author	= {S. Scholtes and M. St\"ohr},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathofor,
  pages		= {851-863},
  title		= {How stringent is the linear independence condition for
		  mathematical programs with equilibrium constraints?},
  volume	= 26,
  year		= 2001
}

###Article{	  schostoh:2001,
  author	= {S. Scholtes and M. St\"ohr},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= mathofor,
  pages		= {851-863},
  title		= {How stringent is the linear independence condition for
		  mathematical programs with equilibrium constraints?},
  volume	= 26,
  year		= 2001
}

@InProceedings{	  schraudolph:2007,
  author	= {Nicol N. Schraudolph and Jin Yu and Simon G\"unter},
  title		= {\href{http://nic.schraudolph.org/pubs/SchYuGue07.pdf}{ A
		  Stochastic Quasi-{N}ewton Method for Online Convex
		  Optimization}},
  pages		= {436--443},
  editor	= {Marina Meila and Xiaotong Shen},
  booktitle	= {Proc.\ 11$^{th}$ Intl.\ Conf.\ Artificial Intelligence and
		  Statistics (AIStats)},
  address	= {San Juan, Puerto Rico},
  volume	= 2,
  series	= {Workshop Conf.\@ Proc.},
  publisher	= {J.\@ Machine Learning Res.},
  year		= 2007,
  b2h_type	= {Top Conferences},
  b2h_topic	= {>Quasi-Newton Methods},
  abstract	= { We develop stochastic variants of the well-known BFGS
		  quasi-Newton optimization method, in both full and
		  memory-limited (LBFGS) forms, for online optimization of
		  convex functions. The resulting algorithm performs
		  comparably to a well-tuned natural gradient descent but is
		  scalable to very high-dimensional problems. On standard
		  benchmarks in natural language processing, it
		  asymptotically outperforms previous stochastic gradient
		  methods for parameter estimation in conditional random
		  fields. We are working on analyzing the convergence of
		  online (L)BFGS, and extending it to non-convex optimization
		  problems. }
}

@Article{	  sdpt3:2003,
  author	= {Reha H T\"ut\"unc\"u and Kim Chuan Toh and Michael J. Todd},
  title		= {Solving semidefinite-quadratic-linear programs using
		  {SDPT3}},
  journal	= mathprog,
  volume	= 95,
  year		= 2003,
  pages		= {189--217}
}

###Article{	  sdpt3:2003,
  author	= {Reha H T\"ut\"unc\"u and Kim Chuan Toh and Michael J. Todd},
  title		= {Solving semidefinite-quadratic-linear programs using
		  {SDPT3}},
  journal	= mathprog,
  volume	= 95,
  year		= 2003,
  pages		= {189--217}
}

###Article{	  sdpt3:2003,
  author	= {Reha H T\"ut\"unc\"u and Kim Chuan Toh and Michael J. Todd},
  title		= {Solving semidefinite-quadratic-linear programs using
		  {SDPT3}},
  journal	= mathprog,
  volume	= 95,
  year		= 2003,
  pages		= {189--217}
}

@Article{	  serfling:1974,
  title		= {Probability inequalities for the sum in sampling without
		  replacement},
  author	= {Serfling, R.J.},
  journal	= annalsstats,
  volume	= 2,
  number	= 1,
  pages		= {39--48},
  year		= 1974,
  publisher	= {Institute of Mathematical Statistics}
}

@Article{	  seti:1992,
  author	= {R. Setiono},
  date-modified	= {2007-10-20 15:06:49 -0700},
  journal	= jota,
  number	= 3,
  pages		= {425--444},
  title		= {Interior proximal point algorithm for linear programs},
  volume	= 74,
  year		= 1992
}

###Article{	  seti:1992,
  author	= {R. Setiono},
  date-modified	= {2007-10-20 15:06:49 -0700},
  journal	= jota,
  number	= 3,
  pages		= {425--444},
  title		= {Interior proximal point algorithm for linear programs},
  volume	= 74,
  year		= 1992
}

###Article{	  seti:1992,
  author	= {R. Setiono},
  date-modified	= {2007-10-20 15:06:49 -0700},
  journal	= jota,
  number	= 3,
  pages		= {425--444},
  title		= {Interior proximal point algorithm for linear programs},
  volume	= 74,
  year		= 1992
}

@MastersThesis{	  shan:2008,
  address	= {Vancouver},
  author	= {Shidong Shan},
  month		= {August},
  school	= {Dept. Computer Science, University of British Columbia},
  title		= {A {Levenberg-Marquardt} method for large-scale
		  bound-constrained nonlinear least-squares},
  year		= 2008
}

###MastersThesis{ shan:2008,
  address	= {Vancouver},
  author	= {Shidong Shan},
  month		= {August},
  school	= {Dept. Computer Science, University of British Columbia},
  title		= {A {Levenberg-Marquardt} method for large-scale
		  bound-constrained nonlinear least-squares},
  year		= 2008
}

###MastersThesis{ shan:2008,
  address	= {Vancouver},
  author	= {Shidong Shan},
  month		= {August},
  school	= {Dept. Computer Science, University of British Columbia},
  title		= {A {Levenberg-Marquardt} method for large-scale
		  bound-constrained nonlinear least-squares},
  year		= 2008
}

@Article{	  shannophua:1978,
  author	= {Shanno, D. F. and Phua, K.-H.},
  affiliation	= {The University of Arizona Tucson AZ USA},
  title		= {Matrix conditioning and nonlinear optimization},
  journal	= {Mathematical Programming},
  publisher	= {Springer Berlin / Heidelberg},
  issn		= {0025-5610},
  keyword	= {Mathematics and Statistics},
  pages		= {149-160},
  volume	= 14,
  issue		= 1,
  url		= {http://dx.doi.org/10.1007/BF01588962},
  note		= {10.1007/BF01588962},
  abstract	= {In a series of recent papers, Oren, Oren and Luenberger,
		  Oren and Spedicato, and Spedicato have developed the
		  self-scaling variable metric algorithms. These algorithms
		  alter Broyden's single parameter family of approximations
		  to the inverse Hessian to a double parameter family.
		  Conditions are given on the new parameter to minimize a
		  bound on the condition number of the approximated inverse
		  Hessian while insuring improved step-wise convergence.},
  year		= 1978
}

###Article{	  shannophua:1978,
  author	= {Shanno, D. F. and Phua, K.-H.},
  affiliation	= {The University of Arizona Tucson AZ USA},
  title		= {Matrix conditioning and nonlinear optimization},
  journal	= {Mathematical Programming},
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {149-160},
  volume	= 14,
  issue		= 1,
  url		= {http://dx.doi.org/10.1007/BF01588962},
  note		= {10.1007/BF01588962},
  abstract	= {In a series of recent papers, Oren, Oren and Luenberger,
		  Oren and Spedicato, and Spedicato have developed the
		  self-scaling variable metric algorithms. These algorithms
		  alter Broyden's single parameter family of approximations
		  to the inverse Hessian to a double parameter family.
		  Conditions are given on the new parameter to minimize a
		  bound on the condition number of the approximated inverse
		  Hessian while insuring improved step-wise convergence.},
  year		= 1978
}

###Article{	  shannophua:1978,
  author	= {Shanno, D. F. and Phua, K.-H.},
  affiliation	= {The University of Arizona Tucson AZ USA},
  title		= {Matrix conditioning and nonlinear optimization},
  journal	= {Mathematical Programming},
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {149-160},
  volume	= 14,
  issue		= 1,
  url		= {http://dx.doi.org/10.1007/BF01588962},
  note		= {10.1007/BF01588962},
  abstract	= {In a series of recent papers, Oren, Oren and Luenberger,
		  Oren and Spedicato, and Spedicato have developed the
		  self-scaling variable metric algorithms. These algorithms
		  alter Broyden's single parameter family of approximations
		  to the inverse Hessian to a double parameter family.
		  Conditions are given on the new parameter to minimize a
		  bound on the condition number of the approximated inverse
		  Hessian while insuring improved step-wise convergence.},
  year		= 1978
}

@Article{	  shanvand:1999,
  author	= {D. Shanno and R. Vanderbei},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= compapplopt,
  pages		= {231--252},
  title		= {An interior-point algorithm for nonconvex nonlinear
		  programming},
  volume	= 13,
  year		= 1999
}

###Article{	  shanvand:1999,
  author	= {D. Shanno and R. Vanderbei},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= compapplopt,
  pages		= {231--252},
  title		= {An interior-point algorithm for nonconvex nonlinear
		  programming},
  volume	= 13,
  year		= 1999
}

###Article{	  shanvand:1999,
  author	= {D. Shanno and R. Vanderbei},
  date-modified	= {2007-07-18 14:59:32 -0700},
  journal	= compapplopt,
  pages		= {231--252},
  title		= {An interior-point algorithm for nonconvex nonlinear
		  programming},
  volume	= 13,
  year		= 1999
}

@InProceedings{	  shashaza:2005,
  author	= {A. Shashua and T. Hazan},
  booktitle	= {{IEEE} Proceedings of Intl. Conf. Comp. Vision},
  date-added	= {2007-10-20 16:04:31 -0700},
  date-modified	= {2007-10-20 16:05:22 -0700},
  title		= {Non-negative tensor factorization with applications to
		  statistics and computer vision},
  year		= 2005
}

###InProceedings{ shashaza:2005,
  author	= {A. Shashua and T. Hazan},
  booktitle	= {{IEEE} Proceedings of Intl. Conf. Comp. Vision},
  date-added	= {2007-10-20 16:04:31 -0700},
  date-modified	= {2007-10-20 16:05:22 -0700},
  title		= {Non-negative tensor factorization with applications to
		  statistics and computer vision},
  year		= 2005
}

###InProceedings{ shashaza:2005,
  author	= {A. Shashua and T. Hazan},
  booktitle	= {{IEEE} Proceedings of Intl. Conf. Comp. Vision},
  date-added	= {2007-10-20 16:04:31 -0700},
  date-modified	= {2007-10-20 16:05:22 -0700},
  title		= {Non-negative tensor factorization with applications to
		  statistics and computer vision},
  year		= 2005
}

@Unpublished{	  shew:1994,
  author	= {J. R. Shewchuk},
  month		= {August},
  note		= {See the author's website: http://www-2.cs.cmu.edu/\tild
		  jrs/},
  title		= {An Introduction to the Conjugate Gradient Method Without
		  the Agonizing Pain},
  year		= 1994
}

###Unpublished{	  shew:1994,
  author	= {J. R. Shewchuk},
  month		= {August},
  note		= {See the author's website: http://www-2.cs.cmu.edu/\tild
		  jrs/},
  title		= {An Introduction to the Conjugate Gradient Method Without
		  the Agonizing Pain},
  year		= 1994
}

###Unpublished{	  shew:1994,
  author	= {J. R. Shewchuk},
  month		= {August},
  note		= {See the author's website: http://www-2.cs.cmu.edu/\tild
		  jrs/},
  title		= {An Introduction to the Conjugate Gradient Method Without
		  the Agonizing Pain},
  year		= 1994
}

@Misc{		  silvlim:2007,
  author	= {V. de Silva and L. Lim},
  howpublished	= {To appear in {\it SIAM J. Matrix Anal. Appl.}},
  title		= {Tensor rank and the ill-posedness of the best low-rank
		  approximation problem},
  year		= 2007
}

###Misc{	  silvlim:2007,
  author	= {V. de Silva and L. Lim},
  howpublished	= {To appear in {\it SIAM J. Matrix Anal. Appl.}},
  title		= {Tensor rank and the ill-posedness of the best low-rank
		  approximation problem},
  year		= 2007
}

###Misc{	  silvlim:2007,
  author	= {V. de Silva and L. Lim},
  howpublished	= {To appear in {\it SIAM J. Matrix Anal. Appl.}},
  title		= {Tensor rank and the ill-posedness of the best low-rank
		  approximation problem},
  year		= 2007
}

@Misc{		  slimpy:2007,
  key		= {\url{http://slim.eos.ubc.ca/SLIMpy}},
  author	= {F. Herrmann and S. Ross-Ross},
  title		= {{SlimPy}: {A Python} interface to {Unix}-like pipe based
		  linear operators},
  howpublished	= {\url{http://slim.eos.ubc.ca/SLIMpy}},
  year		= 2007
}

###Misc{	  slimpy:2007,
  key		= {\url{http://slim.eos.ubc.ca/SLIMpy}},
  author	= {F. Herrmann and S. Ross-Ross},
  title		= {{SlimPy}: {A Python} interface to {Unix}-like pipe based
		  linear operators},
  howpublished	= {\url{http://slim.eos.ubc.ca/SLIMpy}},
  year		= 2007
}

###Misc{	  slimpy:2007,
  key		= {\url{http://slim.eos.ubc.ca/SLIMpy}},
  author	= {F. Herrmann and S. Ross-Ross},
  title		= {{SlimPy}: {A Python} interface to {Unix}-like pipe based
		  linear operators},
  howpublished	= {\url{http://slim.eos.ubc.ca/SLIMpy}},
  year		= 2007
}

@Book{		  smit:2004,
  address	= {Hoboken, NJ},
  author	= {R. Martin Smith},
  publisher	= johnwileysons,
  title		= {Understanding mass spectra: A basic approach},
  edition	= {Second},
  year		= 2004
}

###Book{	  smit:2004,
  address	= {Hoboken, NJ},
  author	= {R. Martin Smith},
  publisher	= johnwileysons,
  title		= {Understanding mass spectra: A basic approach},
  edition	= {Second},
  year		= 2004
}

###Book{	  smit:2004,
  address	= {Hoboken, NJ},
  author	= {R. Martin Smith},
  publisher	= johnwileysons,
  title		= {Understanding mass spectra: A basic approach},
  edition	= {Second},
  year		= 2004
}

@Unpublished{	  so:2013,
  author	= {Anthony Man-Cho So},
  title		= {Non-asymptotic convergence analysis of inexact gradient
		  methods for machine learning without strong convexity},
  month		= {August},
  year		= {2013},
  note		= {\url{http://www.se.cuhk.edu.hk/~manchoso/papers/inexact_GM_conv.pdf}}
}

@Article{	  sol98,
  author	= "Solodov, M. V.",
  title		= "Incremental gradient algorithms with step sizes bounded
		  away from zero",
  journal	= "Comput. Optim. Appl.",
  year		= 1998,
  volume	= 11,
  page		= {23--35}
}

###Article{	  sol98,
  author	= "Solodov, M. V.",
  title		= "Incremental gradient algorithms with step sizes bounded
		  away from zero",
  journal	= "Comput. Optim. Appl.",
  year		= 1998,
  volume	= 11,
  page		= {23--35}
}

###Article{	  sol98,
  author	= "Solodov, M. V.",
  title		= "Incremental gradient algorithms with step sizes bounded
		  away from zero",
  journal	= "Comput. Optim. Appl.",
  year		= 1998,
  volume	= 11,
  page		= {23--35}
}

@Article{	  solodov1998incremental,
  title		= {{Incremental gradient algorithms with stepsizes bounded
		  away from zero}},
  author	= {Solodov, M.V.},
  journal	= {Computational Optimization and Applications},
  volume	= 11,
  number	= 1,
  pages		= {23--35},
  year		= 1998,
  publisher	= {Springer}
}

###Article{	  solodov1998incremental,
  title		= {{Incremental gradient algorithms with stepsizes bounded
		  away from zero}},
  author	= {Solodov, M.V.},
  journal	= {Computational Optimization and Applications},
  volume	= 11,
  number	= 1,
  pages		= {23--35},
  year		= 1998,
  publisher	= {Springer}
}

###Article{	  solodov1998incremental,
  title		= {{Incremental gradient algorithms with stepsizes bounded
		  away from zero}},
  author	= {Solodov, M.V.},
  journal	= {Computational Optimization and Applications},
  volume	= 11,
  number	= 1,
  pages		= {23--35},
  year		= 1998,
  publisher	= {Springer}
}

@Article{	  solodov2003approximations,
  title		= {On approximations with finite precision in bundle methods
		  for nonsmooth optimization},
  author	= {Solodov, Mikhail V},
  journal	= {Journal of Optimization Theory and Applications},
  volume	= {119},
  number	= {1},
  pages		= {151--165},
  year		= {2003},
  publisher	= {Springer}
}

@Article{	  soy07,
  author	= "So, A. M.-C. and Yinyu Ye",
  title		= "Theory of semidefinite programming for sensor network
		  localization",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 109,
  page		= {367--384}
}

###Article{	  soy07,
  author	= "So, A. M.-C. and Yinyu Ye",
  title		= "Theory of semidefinite programming for sensor network
		  localization",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 109,
  page		= {367--384}
}

###Article{	  soy07,
  author	= "So, A. M.-C. and Yinyu Ye",
  title		= "Theory of semidefinite programming for sensor network
		  localization",
  journal	= "Math. Program.",
  year		= 2007,
  volume	= 109,
  page		= {367--384}
}

@Misc{		  sparselab,
  author	= {David L. Donoho and Iddo Driori and Victoria C. Stodden
		  and Yaakov Tsaig},
  date-added	= {2007-12-13 11:47:57 -0800},
  date-modified	= {2007-12-13 11:47:57 -0800},
  howpublished	= {\url{http://sparselab.stanford.edu/}},
  title		= {Sparselab},
  year		= 2007
}

###Misc{	  sparselab,
  author	= {David L. Donoho and Iddo Driori and Victoria C. Stodden
		  and Yaakov Tsaig},
  date-added	= {2007-12-13 11:47:57 -0800},
  date-modified	= {2007-12-13 11:47:57 -0800},
  howpublished	= {\url{http://sparselab.stanford.edu/}},
  title		= {Sparselab},
  year		= 2007
}

###Misc{	  sparselab,
  author	= {David L. Donoho and Iddo Driori and Victoria C. Stodden
		  and Yaakov Tsaig},
  date-added	= {2007-12-13 11:47:57 -0800},
  date-modified	= {2007-12-13 11:47:57 -0800},
  howpublished	= {\url{http://sparselab.stanford.edu/}},
  title		= {Sparselab},
  year		= 2007
}

@Misc{		  spec:2009,
  author	= {Eckard Specht},
  title		= {Packing of circles in the unit circle},
  url		= {http://hydra.nat.uni-magdeburg.de/packing/cci/cci.html},
  year		= 2009
}

###Misc{	  spec:2009,
  author	= {Eckard Specht},
  title		= {Packing of circles in the unit circle},
  url		= {http://hydra.nat.uni-magdeburg.de/packing/cci/cci.html},
  year		= 2009
}

###Misc{	  spec:2009,
  author	= {Eckard Specht},
  title		= {Packing of circles in the unit circle},
  url		= {http://hydra.nat.uni-magdeburg.de/packing/cci/cci.html},
  year		= 2009
}

@Article{	  springerlink:10.1007/s10107-009-0306-5,
  author	= {Ma, Shiqian and Goldfarb, Donald and Chen, Lifeng},
  affiliation	= {Department of Industrial Engineering and Operations
		  Research, Columbia University, New York, NY 10027, USA},
  title		= {Fixed point and Bregman iterative methods for matrix rank
		  minimization},
  journal	= {Mathematical Programming},
  publisher	= {Springer Berlin / Heidelberg},
  issn		= {0025-5610},
  keyword	= {Mathematics and Statistics},
  pages		= {321-353},
  volume	= 128,
  issue		= 1,
  url		= {http://dx.doi.org/10.1007/s10107-009-0306-5},
  note		= {10.1007/s10107-009-0306-5},
  abstract	= {The linearly constrained matrix rank minimization problem
		  is widely applicable in many fields such as control, signal
		  processing and system identification. The tightest convex
		  relaxation of this problem is the linearly constrained
		  nuclear norm minimization. Although the latter can be cast
		  as a semidefinite programming problem, such an approach is
		  computationally expensive to solve when the matrices are
		  large. In this paper, we propose fixed point and Bregman
		  iterative algorithms for solving the nuclear norm
		  minimization problem and prove convergence of the first of
		  these algorithms. By using a homotopy approach together
		  with an approximate singular value decomposition procedure,
		  we get a very fast, robust and powerful algorithm, which we
		  call FPCA (Fixed Point Continuation with Approximate SVD),
		  that can solve very large matrix rank minimization problems
		  (the code can be downloaded from
		  http://www.columbia.edu/~sm2756/FPCA.htm for non-commercial
		  use). Our numerical results on randomly generated and real
		  matrix completion problems demonstrate that this algorithm
		  is much faster and provides much better recoverability than
		  semidefinite programming solvers such as SDPT3. For
		  example, our algorithm can recover 1000 × 1000 matrices of
		  rank 50 with a relative error of 10 −5 in about 3 min by
		  sampling only 20\% of the elements. We know of no other
		  method that achieves as good recoverability. Numerical
		  experiments on online recommendation, DNA microarray data
		  set and image inpainting problems demonstrate the
		  effectiveness of our algorithms.},
  year		= 2011
}

###Article{	  springerlink:10.1007/s10107-009-0306-5,
  author	= {Ma, Shiqian and Goldfarb, Donald and Chen, Lifeng},
  affiliation	= {Department of Industrial Engineering and Operations
		  Research, Columbia University, New York, NY 10027, USA},
  title		= {Fixed point and {Bregman} iterative methods for matrix
		  rank minimization},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {321-353},
  volume	= 128,
  issue		= 1,
  doi		= {10.1007/s10107-009-0306-5},
  abstract	= {The linearly constrained matrix rank minimization problem
		  is widely applicable in many fields such as control, signal
		  processing and system identification. The tightest convex
		  relaxation of this problem is the linearly constrained
		  nuclear norm minimization. Although the latter can be cast
		  as a semidefinite programming problem, such an approach is
		  computationally expensive to solve when the matrices are
		  large. In this paper, we propose fixed point and Bregman
		  iterative algorithms for solving the nuclear norm
		  minimization problem and prove convergence of the first of
		  these algorithms. By using a homotopy approach together
		  with an approximate singular value decomposition procedure,
		  we get a very fast, robust and powerful algorithm, which we
		  call FPCA (Fixed Point Continuation with Approximate SVD),
		  that can solve very large matrix rank minimization problems
		  (the code can be downloaded from
		  http://www.columbia.edu/~sm2756/FPCA.htm for non-commercial
		  use). Our numerical results on randomly generated and real
		  matrix completion problems demonstrate that this algorithm
		  is much faster and provides much better recoverability than
		  semidefinite programming solvers such as SDPT3. For
		  example, our algorithm can recover 1000 × 1000 matrices of
		  rank 50 with a relative error of 10 −5 in about 3 min by
		  sampling only 20\% of the elements. We know of no other
		  method that achieves as good recoverability. Numerical
		  experiments on online recommendation, DNA microarray data
		  set and image inpainting problems demonstrate the
		  effectiveness of our algorithms.},
  year		= 2011
}

###Article{	  springerlink:10.1007/s10107-009-0306-5,
  author	= {Ma, Shiqian and Goldfarb, Donald and Chen, Lifeng},
  affiliation	= {Department of Industrial Engineering and Operations
		  Research, Columbia University, New York, NY 10027, USA},
  title		= {Fixed point and {Bregman} iterative methods for matrix
		  rank minimization},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {321-353},
  volume	= 128,
  issue		= 1,
  doi		= {10.1007/s10107-009-0306-5},
  abstract	= {The linearly constrained matrix rank minimization problem
		  is widely applicable in many fields such as control, signal
		  processing and system identification. The tightest convex
		  relaxation of this problem is the linearly constrained
		  nuclear norm minimization. Although the latter can be cast
		  as a semidefinite programming problem, such an approach is
		  computationally expensive to solve when the matrices are
		  large. In this paper, we propose fixed point and Bregman
		  iterative algorithms for solving the nuclear norm
		  minimization problem and prove convergence of the first of
		  these algorithms. By using a homotopy approach together
		  with an approximate singular value decomposition procedure,
		  we get a very fast, robust and powerful algorithm, which we
		  call FPCA (Fixed Point Continuation with Approximate SVD),
		  that can solve very large matrix rank minimization problems
		  (the code can be downloaded from
		  http://www.columbia.edu/~sm2756/FPCA.htm for non-commercial
		  use). Our numerical results on randomly generated and real
		  matrix completion problems demonstrate that this algorithm
		  is much faster and provides much better recoverability than
		  semidefinite programming solvers such as SDPT3. For
		  example, our algorithm can recover 1000 × 1000 matrices of
		  rank 50 with a relative error of 10 −5 in about 3 min by
		  sampling only 20\% of the elements. We know of no other
		  method that achieves as good recoverability. Numerical
		  experiments on online recommendation, DNA microarray data
		  set and image inpainting problems demonstrate the
		  effectiveness of our algorithms.},
  year		= 2011
}

@Article{	  springerlink:10.1007/s10107-010-0437-8,
  author	= {Liu, Yong-Jin and Sun, Defeng and Toh, Kim-Chuan},
  affiliation	= {Faculty of Science, Shenyang Aerospace University, 110136
		  Shenyang, People’s Republic of China},
  title		= {An implementable proximal point algorithmic framework for
		  nuclear norm minimization},
  journal	= {Mathematical Programming},
  publisher	= {Springer Berlin / Heidelberg},
  issn		= {0025-5610},
  keyword	= {Mathematics and Statistics},
  pages		= {1-38},
  url		= {http://dx.doi.org/10.1007/s10107-010-0437-8},
  note		= {10.1007/s10107-010-0437-8},
  abstract	= {The nuclear norm minimization problem is to find a matrix
		  with the minimum nuclear norm subject to linear and second
		  order cone constraints. Such a problem often arises from
		  the convex relaxation of a rank minimization problem with
		  noisy data, and arises in many fields of engineering and
		  science. In this paper, we study inexact proximal point
		  algorithms in the primal, dual and primal-dual forms for
		  solving the nuclear norm minimization with linear equality
		  and second order cone constraints. We design efficient
		  implementations of these algorithms and present
		  comprehensive convergence results. In particular, we
		  investigate the performance of our proposed algorithms in
		  which the inner sub-problems are approximately solved by
		  the gradient projection method or the accelerated proximal
		  gradient method. Our numerical results for solving randomly
		  generated matrix completion problems and real matrix
		  completion problems show that our algorithms perform
		  favorably in comparison to several recently proposed
		  state-of-the-art algorithms. Interestingly, our proposed
		  algorithms are connected with other algorithms that have
		  been studied in the literature.}
}

###Article{	  springerlink:10.1007/s10107-010-0437-8,
  author	= {Liu, Yong-Jin and Sun, Defeng and Toh, Kim-Chuan},
  affiliation	= {Faculty of Science, Shenyang Aerospace University, 110136
		  Shenyang, People’s Republic of China},
  title		= {An implementable proximal point algorithmic framework for
		  nuclear norm minimization},
  year		= 2012,
  volume	= 133,
  number	= {1--2},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {1-38},
  doi		= {10.1007/s10107-010-0437-8},
  abstract	= {The nuclear norm minimization problem is to find a matrix
		  with the minimum nuclear norm subject to linear and second
		  order cone constraints. Such a problem often arises from
		  the convex relaxation of a rank minimization problem with
		  noisy data, and arises in many fields of engineering and
		  science. In this paper, we study inexact proximal point
		  algorithms in the primal, dual and primal-dual forms for
		  solving the nuclear norm minimization with linear equality
		  and second order cone constraints. We design efficient
		  implementations of these algorithms and present
		  comprehensive convergence results. In particular, we
		  investigate the performance of our proposed algorithms in
		  which the inner sub-problems are approximately solved by
		  the gradient projection method or the accelerated proximal
		  gradient method. Our numerical results for solving randomly
		  generated matrix completion problems and real matrix
		  completion problems show that our algorithms perform
		  favorably in comparison to several recently proposed
		  state-of-the-art algorithms. Interestingly, our proposed
		  algorithms are connected with other algorithms that have
		  been studied in the literature.}
}

###Article{	  springerlink:10.1007/s10107-010-0437-8,
  author	= {Liu, Yong-Jin and Sun, Defeng and Toh, Kim-Chuan},
  affiliation	= {Faculty of Science, Shenyang Aerospace University, 110136
		  Shenyang, People’s Republic of China},
  title		= {An implementable proximal point algorithmic framework for
		  nuclear norm minimization},
  year		= 2012,
  volume	= 133,
  number	= {1--2},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {1-38},
  doi		= {10.1007/s10107-010-0437-8},
  abstract	= {The nuclear norm minimization problem is to find a matrix
		  with the minimum nuclear norm subject to linear and second
		  order cone constraints. Such a problem often arises from
		  the convex relaxation of a rank minimization problem with
		  noisy data, and arises in many fields of engineering and
		  science. In this paper, we study inexact proximal point
		  algorithms in the primal, dual and primal-dual forms for
		  solving the nuclear norm minimization with linear equality
		  and second order cone constraints. We design efficient
		  implementations of these algorithms and present
		  comprehensive convergence results. In particular, we
		  investigate the performance of our proposed algorithms in
		  which the inner sub-problems are approximately solved by
		  the gradient projection method or the accelerated proximal
		  gradient method. Our numerical results for solving randomly
		  generated matrix completion problems and real matrix
		  completion problems show that our algorithms perform
		  favorably in comparison to several recently proposed
		  state-of-the-art algorithms. Interestingly, our proposed
		  algorithms are connected with other algorithms that have
		  been studied in the literature.}
}

@Article{	  sth03,
  author	= "Strohmer, T. and Heath, R. Jr.",
  title		= "Grassmannian frames with applications to coding and
		  communications.",
  journal	= "Appl. Comp. Harm. Anal.",
  year		= 2003,
  volume	= 14,
  page		= {257--275}
}

###Article{	  sth03,
  author	= "Strohmer, T. and Heath, R. Jr.",
  title		= "Grassmannian frames with applications to coding and
		  communications.",
  journal	= "Appl. Comp. Harm. Anal.",
  year		= 2003,
  volume	= 14,
  page		= {257--275}
}

###Article{	  sth03,
  author	= "Strohmer, T. and Heath, R. Jr.",
  title		= "Grassmannian frames with applications to coding and
		  communications.",
  journal	= "Appl. Comp. Harm. Anal.",
  year		= 2003,
  volume	= 14,
  page		= {257--275}
}

@Unpublished{	  stojparvhass:2008,
  author	= {M. Stojnic and F. Parvaresh and B. Hassibi},
  title		= {On the reconstruction of block-sparse signals with an
		  optimal number of measurements},
  month		= {March},
  year		= 2008,
  note		= {Available at arXiv 0804.0041}
}

###Unpublished{	  stojparvhass:2008,
  author	= {M. Stojnic and F. Parvaresh and B. Hassibi},
  title		= {On the reconstruction of block-sparse signals with an
		  optimal number of measurements},
  month		= {March},
  year		= 2008,
  note		= {Available at arXiv 0804.0041}
}

###Unpublished{	  stojparvhass:2008,
  author	= {M. Stojnic and F. Parvaresh and B. Hassibi},
  title		= {On the reconstruction of block-sparse signals with an
		  optimal number of measurements},
  month		= {March},
  year		= 2008,
  note		= {Available at arXiv 0804.0041}
}

@TechReport{	  stur:2001,
  author	= {J. F. Sturm},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Econometrics, Tilburg University, Tilburg,
		  The Netherlands},
  title		= {Using {SeDuMi} 1.02, A {Matlab} toolbox for optimization
		  over symmetric cones (updated for {V}ersion 1.05)},
  year		= {August 1998 -- October 2001}
}

###TechReport{	  stur:2001,
  author	= {J. F. Sturm},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Econometrics, Tilburg University, Tilburg,
		  The Netherlands},
  title		= {Using {SeDuMi} 1.02, A {Matlab} toolbox for optimization
		  over symmetric cones (updated for {V}ersion 1.05)},
  year		= {August 1998 -- October 2001}
}

###TechReport{	  stur:2001,
  author	= {J. F. Sturm},
  date-modified	= {2007-07-18 14:59:34 -0700},
  institution	= {Department of Econometrics, Tilburg University, Tilburg,
		  The Netherlands},
  title		= {Using {SeDuMi} 1.02, A {Matlab} toolbox for optimization
		  over symmetric cones (updated for {V}ersion 1.05)},
  year		= {August 1998 -- October 2001}
}

@Article{	  sturlies:2005,
  author	= {Eric de Sturler and J\"{o}rg Liesen},
  doi		= {10.1137/S1064827502411006},
  journal	= siamcomp,
  keywords	= {saddle point systems; indefinite systems; eigenvalue
		  bounds; Krylov subspace methods; preconditioning;
		  constrained optimization; mesh-flattening},
  number	= 5,
  pages		= {1598-1619},
  publisher	= {SIAM},
  title		= {Block-Diagonal and Constraint Preconditioners for
		  Nonsymmetric Indefinite Linear Systems. Part I: Theory},
  url		= {http://link.aip.org/link/?SCE/26/1598/1},
  volume	= 26,
  year		= 2005
}

###Article{	  sturlies:2005,
  author	= {Eric de Sturler and J\"{o}rg Liesen},
  doi		= {10.1137/S1064827502411006},
  journal	= siamcomp,
  keywords	= {saddle point systems; indefinite systems; eigenvalue
		  bounds; Krylov subspace methods; preconditioning;
		  constrained optimization; mesh-flattening},
  number	= 5,
  pages		= {1598-1619},
  publisher	= {SIAM},
  title		= {Block-Diagonal and Constraint Preconditioners for
		  Nonsymmetric Indefinite Linear Systems. Part I: Theory},
  url		= {http://link.aip.org/link/?SCE/26/1598/1},
  volume	= 26,
  year		= 2005
}

###Article{	  sturlies:2005,
  author	= {Eric de Sturler and J\"{o}rg Liesen},
  doi		= {10.1137/S1064827502411006},
  journal	= siamcomp,
  keywords	= {saddle point systems; indefinite systems; eigenvalue
		  bounds; Krylov subspace methods; preconditioning;
		  constrained optimization; mesh-flattening},
  number	= 5,
  pages		= {1598-1619},
  publisher	= {SIAM},
  title		= {Block-Diagonal and Constraint Preconditioners for
		  Nonsymmetric Indefinite Linear Systems. Part I: Theory},
  url		= {http://link.aip.org/link/?SCE/26/1598/1},
  volume	= 26,
  year		= 2005
}

@Misc{		  surfacelettoolbox,
  author	= {Y. Lu},
  howpublished	= {\url{http://www.mathworks.com/matlabcentral/fileexchange/14485}},
  title		= {{Surfacelet toolbox}},
  year		= 2008
}

###Misc{	  surfacelettoolbox,
  author	= {Y. Lu},
  howpublished	= {\url{http://www.mathworks.com/matlabcentral/fileexchange/14485}},
  title		= {{Surfacelet toolbox}},
  year		= 2008
}

###Misc{	  surfacelettoolbox,
  author	= {Y. Lu},
  howpublished	= {\url{http://www.mathworks.com/matlabcentral/fileexchange/14485}},
  title		= {{Surfacelet toolbox}},
  year		= 2008
}

@Unpublished{	  swanson:2013:cond-layer-cake,
  author	= {Jason Swanson},
  title		= {Conditional expectation},
  note		= {Unpublished lecture notes,
		  \url{http://www.swansonsite.com/W/instructional/condexp.pdf}},
  month		= {April},
  year		= 2009
}

@TechReport{	  syos08,
  author	= "J. Shi and W. Yin and S. Osher and P. Sajda",
  title		= "A fast algorithm for large scale {L1}-regularized logistic
		  regression",
  institution	= "Department of Computational and Applied Mathematics, Rice
		  University",
  address	= "Houston",
  year		= 2008
}

###TechReport{	  syos08,
  author	= "J. Shi and W. Yin and S. Osher and P. Sajda",
  title		= "A fast algorithm for large scale {L1}-regularized logistic
		  regression",
  institution	= "Department of Computational and Applied Mathematics, Rice
		  University",
  address	= "Houston",
  year		= 2008
}

###TechReport{	  syos08,
  author	= "J. Shi and W. Yin and S. Osher and P. Sajda",
  title		= "A fast algorithm for large scale {L1}-regularized logistic
		  regression",
  institution	= "Department of Computational and Applied Mathematics, Rice
		  University",
  address	= "Houston",
  year		= 2008
}

@InProceedings{	  takhlaskwakietal:2006,
  author	= {D. Takhar and J. N. Laska and M. Wakin and M. Duarte and
		  D. Baron and S. Sarvotham and K. K. Kelly and R. G.
		  Baraniuk},
  booktitle	= {Proceedings of the IS\&T/SPIE Symposium on Electronic
		  Imaging: Computational Imaging},
  month		= {January},
  title		= {A new camera architecture based on optical-domain
		  compression},
  volume	= 6065,
  year		= 2006
}

###InProceedings{ takhlaskwakietal:2006,
  author	= {D. Takhar and J. N. Laska and M. Wakin and M. Duarte and
		  D. Baron and S. Sarvotham and K. K. Kelly and R. G.
		  Baraniuk},
  booktitle	= {Proceedings of the IS\&T/SPIE Symposium on Electronic
		  Imaging: Computational Imaging},
  month		= {January},
  title		= {A new camera architecture based on optical-domain
		  compression},
  volume	= 6065,
  year		= 2006
}

###InProceedings{ takhlaskwakietal:2006,
  author	= {D. Takhar and J. N. Laska and M. Wakin and M. Duarte and
		  D. Baron and S. Sarvotham and K. K. Kelly and R. G.
		  Baraniuk},
  booktitle	= {Proceedings of the IS\&T/SPIE Symposium on Electronic
		  Imaging: Computational Imaging},
  month		= {January},
  title		= {A new camera architecture based on optical-domain
		  compression},
  volume	= 6065,
  year		= 2006
}

@Article{	  tang2013efficiently,
  title		= {Efficiently Using Second Order Information in Large l1
		  Regularization Problems},
  author	= {Tang, Xiaocheng and Scheinberg, Katya},
  journal	= {arXiv preprint arXiv:1303.6935},
  year		= {2013}
}

###Article{	  tang2013efficiently,
  title		= {Efficiently Using Second Order Information in Large l1
		  Regularization Problems},
  author	= {Tang, Xiaocheng and Scheinberg, Katya},
  journal	= {arXiv preprint arXiv:1303.6935},
  year		= {2013}
}

@TechReport{	  tao-user-ref:2007,
  author	= "S. J. Benson and L. Curfman McInnes and J. Mor\'{e} and J.
		  Sarich",
  title		= "{TAO} User Manual (Revision 1.8)",
  year		= 2007,
  institution	= "Mathematics and Computer Science Division, Argonne
		  National Laboratory",
  address	= {Argonne, IL},
  number	= "ANL/MCS-TM-242",
  note		= "\url{http://www.mcs.anl.gov/tao}",
  type		= {Tech. rep.}
}

###TechReport{	  tao-user-ref:2007,
  author	= "S. J. Benson and L. Curfman McInnes and J. Mor\'{e} and J.
		  Sarich",
  title		= "{TAO} User Manual (Revision 1.8)",
  year		= 2007,
  institution	= "Mathematics and Computer Science Division, Argonne
		  National Laboratory",
  address	= {Argonne, IL},
  number	= "ANL/MCS-TM-242",
  note		= "\url{http://www.mcs.anl.gov/tao}",
  type		= {Tech. rep.}
}

###TechReport{	  tao-user-ref:2007,
  author	= "S. J. Benson and L. Curfman McInnes and J. Mor\'{e} and J.
		  Sarich",
  title		= "{TAO} User Manual (Revision 1.8)",
  year		= 2007,
  institution	= "Mathematics and Computer Science Division, Argonne
		  National Laboratory",
  address	= {Argonne, IL},
  number	= "ANL/MCS-TM-242",
  note		= "\url{http://www.mcs.anl.gov/tao}",
  type		= {Tech. rep.}
}

@Article{	  tapi:1977,
  author	= {R. A. Tapia},
  journal	= jota,
  pages		= {135--194},
  title		= {Diagonalized multiplier methods and quasi-{N}ewton methods
		  for constrained optimization},
  volume	= 22,
  year		= 1977
}

###Article{	  tapi:1977,
  author	= {R. A. Tapia},
  journal	= jota,
  pages		= {135--194},
  title		= {Diagonalized multiplier methods and quasi-{N}ewton methods
		  for constrained optimization},
  volume	= 22,
  year		= 1977
}

###Article{	  tapi:1977,
  author	= {R. A. Tapia},
  journal	= jota,
  pages		= {135--194},
  title		= {Diagonalized multiplier methods and quasi-{N}ewton methods
		  for constrained optimization},
  volume	= 22,
  year		= 1977
}

@Article{	  tarasov1988method,
  title		= {The method of inscribed ellipsoids},
  author	= {Tarasov, Khachiyan, Erlikh},
  journal	= {Soviet Mathematics Doklady},
  volume	= {298},
  number	= {5},
  pages		= {1081--1085},
  year		= {1988}
}

@Article{	  teb97,
  author	= "Teboulle, M.",
  title		= "Convergence of proximal-like algorithms",
  journal	= siamopt,
  year		= 1997,
  volume	= 7,
  page		= {1069--1083}
}

###Article{	  teb97,
  author	= "Teboulle, M.",
  title		= "Convergence of proximal-like algorithms",
  journal	= siamopt,
  year		= 1997,
  volume	= 7,
  page		= {1069--1083}
}

###Article{	  teb97,
  author	= "Teboulle, M.",
  title		= "Convergence of proximal-like algorithms",
  journal	= siamopt,
  year		= 1997,
  volume	= 7,
  page		= {1069--1083}
}

@Article{	  teus:2013,
  author	= {T. Teuber and G. Steidl and R. H. Chan},
  title		= {Minimization and parameter estimation for seminorm
		  regularization models with {I}-divergence constraints},
  journal	= {Inverse Probl.},
  volume	= 29,
  number	= 3,
  year		= 2013,
  abstract	= {In this paper, we analyze the minimization of seminorms
		  {$\|L\cdot\|$} on {$\mathbb {R}^n$} under the constraint of
		  a bounded I-divergence $D(b,H\cdot)$ for rather general
		  linear operators $H$ and $L$. The I-divergence is also
		  known as Kullback–Leibler divergence and appears in many
		  models in imaging science, in particular when dealing with
		  Poisson data but also in the case of multiplicative Gamma
		  noise. Often H represents, e.g., a linear blur operator and
		  L is some discrete derivative or frame analysis operator. A
		  central part of this paper consists in proving relations
		  between the parameters of I-divergence constrained and
		  penalized problems. To solve the I-divergence constrained
		  problem, we consider various first-order primal–dual
		  algorithms which reduce the problem to the solution of
		  certain proximal minimization problems in each iteration
		  step. One of these proximation problems is an I-divergence
		  constrained least-squares problem which can be solved based
		  on Morozov’s discrepancy principle by a Newton method. We
		  prove that these algorithms produce not only a sequence of
		  vectors which converges to a minimizer of the constrained
		  problem but also a sequence of parameters which converges
		  to a regularization parameter so that the corresponding
		  penalized problem has the same solution. Furthermore, we
		  derive a rule for automatically setting the constraint
		  parameter for data corrupted by multiplicative Gamma noise.
		  The performance of the various algorithms is finally
		  demonstrated for different image restoration tasks both for
		  images corrupted by Poisson noise and multiplicative Gamma
		  noise.}
}

###Article{	  teus:2013,
  author	= {T. Teuber and G. Steidl and R. H. Chan},
  title		= {Minimization and parameter estimation for seminorm
		  regularization models with {I}-divergence constraints},
  journal	= {Inverse Probl.},
  volume	= 29,
  number	= 3,
  year		= 2013,
  abstract	= {In this paper, we analyze the minimization of seminorms
		  {$\|L\cdot\|$} on {$\mathbb {R}^n$} under the constraint of
		  a bounded I-divergence $D(b,H\cdot)$ for rather general
		  linear operators $H$ and $L$. The I-divergence is also
		  known as Kullback–Leibler divergence and appears in many
		  models in imaging science, in particular when dealing with
		  Poisson data but also in the case of multiplicative Gamma
		  noise. Often H represents, e.g., a linear blur operator and
		  L is some discrete derivative or frame analysis operator. A
		  central part of this paper consists in proving relations
		  between the parameters of I-divergence constrained and
		  penalized problems. To solve the I-divergence constrained
		  problem, we consider various first-order primal–dual
		  algorithms which reduce the problem to the solution of
		  certain proximal minimization problems in each iteration
		  step. One of these proximation problems is an I-divergence
		  constrained least-squares problem which can be solved based
		  on Morozov’s discrepancy principle by a Newton method. We
		  prove that these algorithms produce not only a sequence of
		  vectors which converges to a minimizer of the constrained
		  problem but also a sequence of parameters which converges
		  to a regularization parameter so that the corresponding
		  penalized problem has the same solution. Furthermore, we
		  derive a rule for automatically setting the constraint
		  parameter for data corrupted by multiplicative Gamma noise.
		  The performance of the various algorithms is finally
		  demonstrated for different image restoration tasks both for
		  images corrupted by Poisson noise and multiplicative Gamma
		  noise.}
}

@Article{	  tgs06,
  author	= {Joel A. Tropp and Anna C. Gilbert and Martin J. Strauss},
  title		= {Algorithms for Simultaneous Sparse Approximation: Part I:
		  Greedy Pursuit},
  journal	= {Signal Processing},
  volume	= 86,
  issue		= 3,
  year		= 2006,
  pages		= {572--588}
}

###Article{	  tgs06,
  author	= {Joel A. Tropp and Anna C. Gilbert and Martin J. Strauss},
  title		= {Algorithms for Simultaneous Sparse Approximation: Part I:
		  Greedy Pursuit},
  journal	= {Signal Processing},
  volume	= 86,
  issue		= 3,
  year		= 2006,
  pages		= {572--588}
}

###Article{	  tgs06,
  author	= {Joel A. Tropp and Anna C. Gilbert and Martin J. Strauss},
  title		= {Algorithms for Simultaneous Sparse Approximation: Part I:
		  Greedy Pursuit},
  journal	= {Signal Processing},
  volume	= 86,
  issue		= 3,
  year		= 2006,
  pages		= {572--588}
}

@Article{	  tib96,
  author	= {R. Tibshirani},
  journal	= {J. R. Statist. Soc. B.},
  number	= 1,
  pages		= {267-288},
  title		= {Regression shrinkage and selection via the {L}asso},
  volume	= 58,
  year		= 1996
}

###Article{	  tib96,
  author	= {R. Tibshirani},
  journal	= {J. R. Statist. Soc. B.},
  number	= 1,
  pages		= {267-288},
  title		= {Regression shrinkage and selection via the {L}asso},
  volume	= 58,
  year		= 1996
}

###Article{	  tib96,
  author	= {R. Tibshirani},
  journal	= {J. R. Statist. Soc. B.},
  number	= 1,
  pages		= {267-288},
  title		= {Regression shrinkage and selection via the {L}asso},
  volume	= 58,
  year		= 1996
}

@Article{	  tibssaunrosszhuknig:2005,
  author	= {Tibshirani, R., Saunders, M., Rosset, S., Zhu, J. and
		  Knight, K.},
  year		= 2005,
  title		= {Sparsity and smoothness via the fused lasso},
  journal	= {J. Royal Statistical Society: Series B (Statistical
		  Methodology)},
  number	= 67,
  pagges	= {91–108},
  doi		= {10.1111/j.1467-9868.2005.00490.x}
}

###Article{	  tibssaunrosszhuknig:2005,
  author	= {R. Tibshirani and M. Saunders and S. Rosset and J. Zhu and
		  K. Knight},
  year		= 2005,
  title		= {Sparsity and smoothness via the fused lasso},
  journal	= {J. Royal Statistical Society: Series B (Statistical
		  Methodology)},
  number	= {Part 1},
  volume	= 67,
  pages		= {91–108},
  doi		= {10.1111/j.1467-9868.2005.00490.x}
}

###Article{	  tibssaunrosszhuknig:2005,
  author	= {R. Tibshirani and M. Saunders and S. Rosset and J. Zhu and
		  K. Knight},
  year		= 2005,
  title		= {Sparsity and smoothness via the fused lasso},
  journal	= {J. Royal Statistical Society: Series B (Statistical
		  Methodology)},
  number	= {Part 1},
  volume	= 67,
  pages		= {91–108},
  doi		= {10.1111/j.1467-9868.2005.00490.x}
}

@Article{	  tik63a,
  author	= {Andrey N. Tikhonov},
  date-modified	= {2007-12-13 11:49:11 -0800},
  journal	= {Soviet Math. Dokl.},
  note		= {English translation of Dokl. Akad. Nauk. SSSR, 153:1
		  (1963), pp 49--52},
  number	= 6,
  pages		= {1624--1647},
  title		= {Regularization of incorrectly posed problems},
  volume	= 4,
  year		= 1963
}

###Article{	  tik63a,
  author	= {Andrey N. Tikhonov},
  date-modified	= {2007-12-13 11:49:11 -0800},
  journal	= {Soviet Math. Dokl.},
  note		= {English translation of Dokl. Akad. Nauk. SSSR, 153:1
		  (1963), pp 49--52},
  number	= 6,
  pages		= {1624--1647},
  title		= {Regularization of incorrectly posed problems},
  volume	= 4,
  year		= 1963
}

###Article{	  tik63a,
  author	= {Andrey N. Tikhonov},
  date-modified	= {2007-12-13 11:49:11 -0800},
  journal	= {Soviet Math. Dokl.},
  note		= {English translation of Dokl. Akad. Nauk. SSSR, 153:1
		  (1963), pp 49--52},
  number	= 6,
  pages		= {1624--1647},
  title		= {Regularization of incorrectly posed problems},
  volume	= 4,
  year		= 1963
}

@Article{	  tikh:1963b,
  author	= {Andrey N. Tikhonov},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Soviet Math. Dokl.},
  note		= {English translation of Dokl. Akad. Nauk. SSSR, 151:3
		  (1963) pp. 501--504},
  number	= 4,
  pages		= {1035--1038},
  title		= {Solution of incorrectly formulated problems and the
		  regularization method},
  volume	= 4,
  year		= 1963
}

###Article{	  tikh:1963b,
  author	= {Andrey N. Tikhonov},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Soviet Math. Dokl.},
  note		= {English translation of Dokl. Akad. Nauk. SSSR, 151:3
		  (1963) pp. 501--504},
  number	= 4,
  pages		= {1035--1038},
  title		= {Solution of incorrectly formulated problems and the
		  regularization method},
  volume	= 4,
  year		= 1963
}

###Article{	  tikh:1963b,
  author	= {Andrey N. Tikhonov},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= {Soviet Math. Dokl.},
  note		= {English translation of Dokl. Akad. Nauk. SSSR, 151:3
		  (1963) pp. 501--504},
  number	= 4,
  pages		= {1035--1038},
  title		= {Solution of incorrectly formulated problems and the
		  regularization method},
  volume	= 4,
  year		= 1963
}

@Book{		  tikharse:1977,
  address	= {Washington, D.C.},
  author	= {A. N. Tikhonov and V. Y. Arsenin},
  date-modified	= {2007-07-18 14:59:34 -0700},
  note		= {Translated from Russian},
  publisher	= {V. H. Winston and Sons},
  title		= {Solutions of Ill-Posed Problems},
  year		= 1977
}

###Book{	  tikharse:1977,
  address	= {Washington, D.C.},
  author	= {A. N. Tikhonov and V. Y. Arsenin},
  date-modified	= {2007-07-18 14:59:34 -0700},
  note		= {Translated from Russian},
  publisher	= {V. H. Winston and Sons},
  title		= {Solutions of Ill-Posed Problems},
  year		= 1977
}

###Book{	  tikharse:1977,
  address	= {Washington, D.C.},
  author	= {A. N. Tikhonov and V. Y. Arsenin},
  date-modified	= {2007-07-18 14:59:34 -0700},
  note		= {Translated from Russian},
  publisher	= {V. H. Winston and Sons},
  title		= {Solutions of Ill-Posed Problems},
  year		= 1977
}

@Article{	  tikhomirov1996evolution,
  title		= {The evolution of methods of convex optimization},
  author	= {Tikhomirov, Vladimir M},
  journal	= {The American mathematical monthly},
  volume	= {103},
  number	= {1},
  pages		= {65--71},
  year		= {1996},
  publisher	= {JSTOR}
}

@Article{	  todd:2001,
  author	= {M. J. Todd},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= actanumerica,
  pages		= {515--560},
  title		= {Semidefinite optimization},
  volume	= 10,
  year		= 2001
}

###Article{	  todd:2001,
  author	= {M. J. Todd},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= actanumerica,
  pages		= {515--560},
  title		= {Semidefinite optimization},
  volume	= 10,
  year		= 2001
}

###Article{	  todd:2001,
  author	= {M. J. Todd},
  date-modified	= {2007-07-18 14:59:34 -0700},
  journal	= actanumerica,
  pages		= {515--560},
  title		= {Semidefinite optimization},
  volume	= 10,
  year		= 2001
}

@PhDThesis{	  toft:1996,
  author	= {P. Toft},
  school	= {Department of Mathematical Modelling, Technical University
		  of Denmark},
  title		= {The Radon transform: Theory and implementation},
  year		= 1996
}

###PhDThesis{	  toft:1996,
  author	= {P. Toft},
  school	= {Department of Mathematical Modelling, Technical University
		  of Denmark},
  title		= {The Radon transform: Theory and implementation},
  year		= 1996
}

###PhDThesis{	  toft:1996,
  author	= {P. Toft},
  school	= {Department of Mathematical Modelling, Technical University
		  of Denmark},
  title		= {The Radon transform: Theory and implementation},
  year		= 1996
}

@TechReport{	  toml:1975,
  author	= {John A. Tomlin},
  institution	= {Department of Operations Research, Stanford University},
  number	= {SOL 75-12},
  title		= {A parametric bounding method for finding a minimum
		  {$\ell_\infty$}-norm solution to a system of equations},
  type		= {Tech. Rep.},
  year		= 1975
}

###TechReport{	  toml:1975,
  author	= {John A. Tomlin},
  institution	= {Department of Operations Research, Stanford University},
  number	= {SOL 75-12},
  title		= {A parametric bounding method for finding a minimum
		  {$\ell_\infty$}-norm solution to a system of equations},
  type		= {Tech. Rep.},
  year		= 1975
}

###TechReport{	  toml:1975,
  author	= {John A. Tomlin},
  institution	= {Department of Operations Research, Stanford University},
  number	= {SOL 75-12},
  title		= {A parametric bounding method for finding a minimum
		  {$\ell_\infty$}-norm solution to a system of equations},
  type		= {Tech. Rep.},
  year		= 1975
}

@TechReport{	  toy09,
  author	= "Toh, K.-C. and Yun, S.",
  title		= "An accelerated proximal gradient algorithm for nuclear
		  regularized least squares problems",
  institution	= "Department of Mathematics, National University of
		  Singapore",
  address	= "Singapore",
  year		= 2009
}

###TechReport{	  toy09,
  author	= "Toh, K.-C. and Yun, S.",
  title		= "An accelerated proximal gradient algorithm for nuclear
		  regularized least squares problems",
  institution	= "Department of Mathematics, National University of
		  Singapore",
  address	= "Singapore",
  year		= 2009
}

###TechReport{	  toy09,
  author	= "Toh, K.-C. and Yun, S.",
  title		= "An accelerated proximal gradient algorithm for nuclear
		  regularized least squares problems",
  institution	= "Department of Mathematics, National University of
		  Singapore",
  address	= "Singapore",
  year		= 2009
}

@Article{	  trg07,
  title		= {Signal Recovery From Random Measurements Via Orthogonal
		  Matching Pursuit},
  author	= {Tropp, J. A and Gilbert, A. C},
  journal	= ieeetransinfo,
  volume	= 53,
  number	= 12,
  pages		= {4655--4666},
  year		= 2007,
  abstract	= {This article demonstrates theoretically and empirically
		  that a greedy algorithm called Orthogonal Matching Pursuit
		  (OMP) can reliably recover a signal with m nonzero entries
		  in dimension $d$ given $O(m ln d)$ random linear
		  measurements of that signal. This is a massive
		  improvementover previous results for OMP, which require
		  $O(m^2)$ measurements. The new results for OMP are
		  comparable with recent results for another algorithm called
		  Basis Pursuit (BP). The OMP algorithm is much faster and
		  much easier to implement, which makes it an attractive
		  alternative to BPfor signal recovery problems}
}

###Article{	  trg07,
  title		= {Signal Recovery From Random Measurements Via Orthogonal
		  Matching Pursuit},
  author	= {Tropp, J. A and Gilbert, A. C},
  journal	= ieeetransinfo,
  volume	= 53,
  number	= 12,
  pages		= {4655--4666},
  year		= 2007,
  abstract	= {This article demonstrates theoretically and empirically
		  that a greedy algorithm called Orthogonal Matching Pursuit
		  (OMP) can reliably recover a signal with m nonzero entries
		  in dimension $d$ given $O(m ln d)$ random linear
		  measurements of that signal. This is a massive
		  improvementover previous results for OMP, which require
		  $O(m^2)$ measurements. The new results for OMP are
		  comparable with recent results for another algorithm called
		  Basis Pursuit (BP). The OMP algorithm is much faster and
		  much easier to implement, which makes it an attractive
		  alternative to BPfor signal recovery problems}
}

###Article{	  trg07,
  title		= {Signal Recovery From Random Measurements Via Orthogonal
		  Matching Pursuit},
  author	= {Tropp, J. A and Gilbert, A. C},
  journal	= ieeetransinfo,
  volume	= 53,
  number	= 12,
  pages		= {4655--4666},
  year		= 2007,
  abstract	= {This article demonstrates theoretically and empirically
		  that a greedy algorithm called Orthogonal Matching Pursuit
		  (OMP) can reliably recover a signal with m nonzero entries
		  in dimension $d$ given $O(m ln d)$ random linear
		  measurements of that signal. This is a massive
		  improvementover previous results for OMP, which require
		  $O(m^2)$ measurements. The new results for OMP are
		  comparable with recent results for another algorithm called
		  Basis Pursuit (BP). The OMP algorithm is much faster and
		  much easier to implement, which makes it an attractive
		  alternative to BPfor signal recovery problems}
}

@Article{	  tro04,
  author	= {Joel A. Tropp},
  date-modified	= {2007-09-15 09:32:05 -0700},
  journal	= {{IEEE} Trans. Inform. Theory},
  month		= {October},
  number	= 10,
  pages		= {2231-2242},
  title		= {Greed is Good: Algorithmic Results for Sparse
		  Approximation},
  volume	= 50,
  year		= 2004
}

###Article{	  tro04,
  author	= {Joel A. Tropp},
  date-modified	= {2007-09-15 09:32:05 -0700},
  journal	= {{IEEE} Trans. Inform. Theory},
  month		= {October},
  number	= 10,
  pages		= {2231-2242},
  title		= {Greed is Good: Algorithmic Results for Sparse
		  Approximation},
  volume	= 50,
  year		= 2004
}

###Article{	  tro04,
  author	= {Joel A. Tropp},
  date-modified	= {2007-09-15 09:32:05 -0700},
  journal	= {{IEEE} Trans. Inform. Theory},
  month		= {October},
  number	= 10,
  pages		= {2231-2242},
  title		= {Greed is Good: Algorithmic Results for Sparse
		  Approximation},
  volume	= 50,
  year		= 2004
}

@Article{	  tro06a,
  abstract	= {This paper studies a difficult and fundamental problem
		  that arises throughout electrical engineering, applied
		  mathematics, and statistics. Suppose that one forms a short
		  linear combination of elementary signals drawn from a
		  large, fixed collection. Given an observation of the linear
		  combination that has been contaminated with additive noise,
		  the goal is to identify which elementary signals
		  participated and to approximate their coefficients.
		  Although many algorithms have been proposed, there is
		  little theory which guarantees that these algorithms can
		  accurately and efficiently solve the problem. This paper
		  studies a method called convex relaxation, which attempts
		  to recover the ideal sparse signal by solving a convex
		  program. This approach is powerful because the optimization
		  can be completed in polynomial time with standard
		  scientific software. The paper provides general conditions
		  which ensure that convex relaxation succeeds. As evidence
		  of the broad impact of these results, the paper describes
		  how convex relaxation can be used for several concrete
		  signal recovery problems. It also describes applications to
		  channel coding, linear regression, and numerical analysis.
		  },
  author	= {Joel A. Tropp},
  journal	= ieeetransinfo,
  keywords	= {Algorithms, approximation methods, basis pursuit, convex
		  program, linear regression, optimization methods,
		  orthogonal matching pursuit, sparse representations},
  month		= {March},
  number	= 3,
  pages		= {1030--1051},
  title		= {Just relax: Convex programming methods for identifying
		  sparse signals in noise},
  volume	= 52,
  year		= 2006
}

###Article{	  tro06a,
  abstract	= {This paper studies a difficult and fundamental problem
		  that arises throughout electrical engineering, applied
		  mathematics, and statistics. Suppose that one forms a short
		  linear combination of elementary signals drawn from a
		  large, fixed collection. Given an observation of the linear
		  combination that has been contaminated with additive noise,
		  the goal is to identify which elementary signals
		  participated and to approximate their coefficients.
		  Although many algorithms have been proposed, there is
		  little theory which guarantees that these algorithms can
		  accurately and efficiently solve the problem. This paper
		  studies a method called convex relaxation, which attempts
		  to recover the ideal sparse signal by solving a convex
		  program. This approach is powerful because the optimization
		  can be completed in polynomial time with standard
		  scientific software. The paper provides general conditions
		  which ensure that convex relaxation succeeds. As evidence
		  of the broad impact of these results, the paper describes
		  how convex relaxation can be used for several concrete
		  signal recovery problems. It also describes applications to
		  channel coding, linear regression, and numerical analysis.
		  },
  author	= {Joel A. Tropp},
  journal	= ieeetransinfo,
  keywords	= {Algorithms, approximation methods, basis pursuit, convex
		  program, linear regression, optimization methods,
		  orthogonal matching pursuit, sparse representations},
  month		= {March},
  number	= 3,
  pages		= {1030--1051},
  title		= {Just relax: Convex programming methods for identifying
		  sparse signals in noise},
  volume	= 52,
  year		= 2006
}

###Article{	  tro06a,
  abstract	= {This paper studies a difficult and fundamental problem
		  that arises throughout electrical engineering, applied
		  mathematics, and statistics. Suppose that one forms a short
		  linear combination of elementary signals drawn from a
		  large, fixed collection. Given an observation of the linear
		  combination that has been contaminated with additive noise,
		  the goal is to identify which elementary signals
		  participated and to approximate their coefficients.
		  Although many algorithms have been proposed, there is
		  little theory which guarantees that these algorithms can
		  accurately and efficiently solve the problem. This paper
		  studies a method called convex relaxation, which attempts
		  to recover the ideal sparse signal by solving a convex
		  program. This approach is powerful because the optimization
		  can be completed in polynomial time with standard
		  scientific software. The paper provides general conditions
		  which ensure that convex relaxation succeeds. As evidence
		  of the broad impact of these results, the paper describes
		  how convex relaxation can be used for several concrete
		  signal recovery problems. It also describes applications to
		  channel coding, linear regression, and numerical analysis.
		  },
  author	= {Joel A. Tropp},
  journal	= ieeetransinfo,
  keywords	= {Algorithms, approximation methods, basis pursuit, convex
		  program, linear regression, optimization methods,
		  orthogonal matching pursuit, sparse representations},
  month		= {March},
  number	= 3,
  pages		= {1030--1051},
  title		= {Just relax: Convex programming methods for identifying
		  sparse signals in noise},
  volume	= 52,
  year		= 2006
}

@Article{	  tro06b,
  author	= {Joel A. Tropp},
  title		= {Algorithms for Simultaneous Sparse Approximation: Part II:
		  Convex Relaxation},
  journal	= {Signal Processing},
  volume	= 86,
  issue		= 3,
  year		= 2006,
  pages		= {589--602}
}

###Article{	  tro06b,
  author	= {Joel A. Tropp},
  title		= {Algorithms for Simultaneous Sparse Approximation: Part II:
		  Convex Relaxation},
  journal	= {Signal Processing},
  volume	= 86,
  issue		= 3,
  year		= 2006,
  pages		= {589--602}
}

###Article{	  tro06b,
  author	= {Joel A. Tropp},
  title		= {Algorithms for Simultaneous Sparse Approximation: Part II:
		  Convex Relaxation},
  journal	= {Signal Processing},
  volume	= 86,
  issue		= 3,
  year		= 2006,
  pages		= {589--602}
}

@Article{	  tropp2004,
  author	= "Tropp, J. A.",
  title		= "Greed is good: algorithmic results for sparse
		  approximation",
  journal	= "IEEE Trans. Inf. Theory",
  year		= 2004,
  volume	= 50,
  page		= {2231--2242}
}

###Article{	  tropp2004,
  author	= "Tropp, J. A.",
  title		= "Greed is good: algorithmic results for sparse
		  approximation",
  journal	= "IEEE Trans. Inf. Theory",
  year		= 2004,
  volume	= 50,
  page		= {2231--2242}
}

###Article{	  tropp2004,
  author	= "Tropp, J. A.",
  title		= "Greed is good: algorithmic results for sparse
		  approximation",
  journal	= "IEEE Trans. Inf. Theory",
  year		= 2004,
  volume	= 50,
  page		= {2231--2242}
}

@InBook{	  tse00,
  author	= "Paul Tseng",
  chapter	= "Error bounds and superlinear convergence analysis of sonic
		  Newton-type methods in optimization",
  title		= "Nonlinear Optimization and Related Topics",
  publisher	= "Kiuwer",
  address	= "Dordrecht",
  year		= 2000,
  page		= {445--462}
}

###InBook{	  tse00,
  author	= "Paul Tseng",
  chapter	= "Error bounds and superlinear convergence analysis of sonic
		  {Newton}-type methods in optimization",
  title		= "Nonlinear Optimization and Related Topics",
  publisher	= "Kiuwer",
  address	= "Dordrecht",
  year		= 2000,
  page		= {445--462}
}

###InBook{	  tse00,
  author	= "Paul Tseng",
  chapter	= "Error bounds and superlinear convergence analysis of sonic
		  Newton-type methods in optimization",
  title		= "Nonlinear Optimization and Related Topics",
  publisher	= "Kiuwer",
  address	= "Dordrecht",
  year		= 2000,
  page		= {445--462}
}

@Article{	  tse00b,
  author	= "Paul Tseng",
  title		= "Convergence of block coordinate descent method for
		  nondifferentiable minimization",
  journal	= jota,
  year		= 2001,
  volume	= 109,
  page		= {473--492}
}

###Article{	  tse00b,
  author	= "Paul Tseng",
  title		= "Convergence of block coordinate descent method for
		  nondifferentiable minimization",
  journal	= jota,
  year		= 2001,
  volume	= 109,
  page		= {473--492}
}

###Article{	  tse00b,
  author	= "Paul Tseng",
  title		= "Convergence of block coordinate descent method for
		  nondifferentiable minimization",
  journal	= jota,
  year		= 2001,
  volume	= 109,
  page		= {473--492}
}

@Article{	  tse07,
  author	= "Paul Tseng",
  title		= "Second-order cone programming relaxation of sensor network
		  localization",
  journal	= siamopt,
  year		= 2007,
  volume	= 18,
  page		= {156--185}
}

###Article{	  tse07,
  author	= "Paul Tseng",
  title		= "Second-order cone programming relaxation of sensor network
		  localization",
  journal	= siamopt,
  year		= 2007,
  volume	= 18,
  page		= {156--185}
}

###Article{	  tse07,
  author	= "Paul Tseng",
  title		= "Second-order cone programming relaxation of sensor network
		  localization",
  journal	= siamopt,
  year		= 2007,
  volume	= 18,
  page		= {156--185}
}

@TechReport{	  tse08,
  author	= "Paul Tseng",
  title		= "On accelerated proximal gradient methods for
		  convex-concave optimization",
  institution	= "Department of Mathematics, University of Washington",
  address	= "Seattle",
  year		= 2008,
  month		= "May",
  note		= "submitted to SIAM J. Optim."
}

###TechReport{	  tse08,
  author	= "Paul Tseng",
  title		= "On accelerated proximal gradient methods for
		  convex-concave optimization",
  institution	= "Department of Mathematics, University of Washington",
  address	= "Seattle",
  year		= 2008,
  month		= "May",
  note		= "submitted to SIAM J. Optim."
}

###TechReport{	  tse08,
  author	= "Paul Tseng",
  title		= "On accelerated proximal gradient methods for
		  convex-concave optimization",
  institution	= "Department of Mathematics, University of Washington",
  address	= "Seattle",
  year		= 2008,
  month		= "May",
  note		= "submitted to SIAM J. Optim."
}

@Article{	  tse09,
  author	= "Paul Tseng",
  title		= "Further results on a stable recovery of sparse
		  overcomplete representations in the presence of noise",
  journal	= ieeetransinfo,
  year		= 2009,
  volume	= 55,
  page		= {888--899}
}

###Article{	  tse09,
  author	= "Paul Tseng",
  title		= "Further results on a stable recovery of sparse
		  overcomplete representations in the presence of noise",
  journal	= ieeetransinfo,
  year		= 2009,
  volume	= 55,
  page		= {888--899}
}

###Article{	  tse09,
  author	= "Paul Tseng",
  title		= "Further results on a stable recovery of sparse
		  overcomplete representations in the presence of noise",
  journal	= ieeetransinfo,
  year		= 2009,
  volume	= 55,
  page		= {888--899}
}

@Article{	  tse91,
  author	= "Paul Tseng",
  title		= "On the rate of convergence of a partially asynchronous
		  gradient projection algorithm",
  journal	= siamopt,
  year		= 1991,
  volume	= 1,
  page		= {603--619}
}

###Article{	  tse91,
  author	= "Paul Tseng",
  title		= "On the rate of convergence of a partially asynchronous
		  gradient projection algorithm",
  journal	= siamopt,
  year		= 1991,
  volume	= 1,
  page		= {603--619}
}

###Article{	  tse91,
  author	= "Paul Tseng",
  title		= "On the rate of convergence of a partially asynchronous
		  gradient projection algorithm",
  journal	= siamopt,
  year		= 1991,
  volume	= 1,
  page		= {603--619}
}

@Article{	  tse93,
  author	= "Paul Tseng",
  title		= "Dual coordinate descent methods for non-strictly convex
		  minimization",
  journal	= "Math. Prog.",
  year		= 1993,
  volume	= 59,
  page		= {231--247}
}

###Article{	  tse93,
  author	= "Paul Tseng",
  title		= "Dual coordinate descent methods for non-strictly convex
		  minimization",
  journal	= "Math. Prog.",
  year		= 1993,
  volume	= 59,
  page		= {231--247}
}

###Article{	  tse93,
  author	= "Paul Tseng",
  title		= "Dual coordinate descent methods for non-strictly convex
		  minimization",
  journal	= "Math. Prog.",
  year		= 1993,
  volume	= 59,
  page		= {231--247}
}

@Article{	  tse98,
  author	= "Paul Tseng",
  title		= "An incremental gradient(-projection) method with momentum
		  term and adaptive stepsize rule",
  journal	= siamopt,
  year		= 1998,
  volume	= 8,
  page		= {506--531}
}

###Article{	  tse98,
  author	= "Paul Tseng",
  title		= "An incremental gradient(-projection) method with momentum
		  term and adaptive stepsize rule",
  journal	= siamopt,
  year		= 1998,
  volume	= 8,
  page		= {506--531}
}

###Article{	  tse98,
  author	= "Paul Tseng",
  title		= "An incremental gradient(-projection) method with momentum
		  term and adaptive stepsize rule",
  journal	= siamopt,
  year		= 1998,
  volume	= 8,
  page		= {506--531}
}

@Article{	  tse99,
  author	= {P. Tseng},
  journal	= {Comput. Optim. Appl.},
  pages		= {221--230},
  title		= {Convergence and error bound for perturbation of linear
		  programs},
  volume	= 13,
  year		= 1999
}

###Article{	  tse99,
  author	= {P. Tseng},
  journal	= {Comput. Optim. Appl.},
  pages		= {221--230},
  title		= {Convergence and error bound for perturbation of linear
		  programs},
  volume	= 13,
  year		= 1999
}

###Article{	  tse99,
  author	= {P. Tseng},
  journal	= {Comput. Optim. Appl.},
  pages		= {221--230},
  title		= {Convergence and error bound for perturbation of linear
		  programs},
  volume	= 13,
  year		= 1999
}

@Article{	  tseng:1998,
  author	= {Paul Tseng},
  title		= {An Incremental Gradient(-Projection) Method with Momentum
		  Term and Adaptive Stepsize Rule},
  publisher	= {SIAM},
  year		= 1998,
  journal	= siamopt,
  volume	= 8,
  number	= 2,
  pages		= {506-531},
  keywords	= {incremental gradient method; gradient projection;
		  convergence analysis; backpropagation; nonlinear neural
		  network training},
  url		= {http://link.aip.org/link/?SJE/8/506/1},
  doi		= {10.1137/S1052623495294797}
}

###Article{	  tseng:1998,
  author	= {Paul Tseng},
  title		= {An Incremental Gradient(-Projection) Method with Momentum
		  Term and Adaptive Stepsize Rule},
  publisher	= {SIAM},
  year		= 1998,
  journal	= siamopt,
  volume	= 8,
  number	= 2,
  pages		= {506-531},
  keywords	= {incremental gradient method; gradient projection;
		  convergence analysis; backpropagation; nonlinear neural
		  network training},
  url		= {http://link.aip.org/link/?SJE/8/506/1},
  doi		= {10.1137/S1052623495294797}
}

###Article{	  tseng:1998,
  author	= {Paul Tseng},
  title		= {An Incremental Gradient(-Projection) Method with Momentum
		  Term and Adaptive Stepsize Rule},
  publisher	= {SIAM},
  year		= 1998,
  journal	= siamopt,
  volume	= 8,
  number	= 2,
  pages		= {506-531},
  keywords	= {incremental gradient method; gradient projection;
		  convergence analysis; backpropagation; nonlinear neural
		  network training},
  url		= {http://link.aip.org/link/?SJE/8/506/1},
  doi		= {10.1137/S1052623495294797}
}

@Article{	  tseng:2010,
  author	= {Tseng, Paul},
  title		= {Approximation accuracy, gradient methods, and error bound
		  for structured convex optimization},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  issn		= {0025-5610},
  keyword	= {Mathematics and Statistics},
  pages		= {263-295},
  volume	= {125},
  issue		= {2},
  doi		= {10.1007/s10107-010-0394-2},
  year		= {2010}
}

###Article{	  tseng:2010,
  author	= {Tseng, Paul},
  title		= {Approximation accuracy, gradient methods, and error bound
		  for structured convex optimization},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {263-295},
  volume	= 125,
  issue		= 2,
  year		= 2010
}

###Article{	  tseng:2010,
  author	= {Tseng, Paul},
  title		= {Approximation accuracy, gradient methods, and error bound
		  for structured convex optimization},
  journal	= mathprog,
  publisher	= {Springer Berlin / Heidelberg},
  keyword	= {Mathematics and Statistics},
  pages		= {263-295},
  volume	= 125,
  issue		= 2,
  year		= 2010
}

@Article{	  tsy08,
  author	= "Paul Tseng and S. Yun",
  title		= "A coordinate gradient descent method for linearly
		  constrained smooth optimization and support vector machines
		  training",
  journal	= "Comput. Optim. Appl.",
  year		= 2008
}

###Article{	  tsy08,
  author	= "Paul Tseng and S. Yun",
  title		= "A coordinate gradient descent method for linearly
		  constrained smooth optimization and support vector machines
		  training",
  journal	= "Comput. Optim. Appl.",
  year		= 2008
}

###Article{	  tsy08,
  author	= "Paul Tseng and S. Yun",
  title		= "A coordinate gradient descent method for linearly
		  constrained smooth optimization and support vector machines
		  training",
  journal	= "Comput. Optim. Appl.",
  year		= 2008
}

@Article{	  tsy09a,
  author	= "Paul Tseng and S. Yun",
  title		= "A coordinate gradient descent method for nonsmooth
		  separable minimization",
  journal	= "Math. Program.",
  year		= 2009,
  volume	= 117,
  page		= {387--423}
}

###Article{	  tsy09a,
  author	= "Paul Tseng and S. Yun",
  title		= "A coordinate gradient descent method for nonsmooth
		  separable minimization",
  journal	= "Math. Program.",
  year		= 2009,
  volume	= 117,
  page		= {387--423}
}

###Article{	  tsy09a,
  author	= "Paul Tseng and S. Yun",
  title		= "A coordinate gradient descent method for nonsmooth
		  separable minimization",
  journal	= "Math. Program.",
  year		= 2009,
  volume	= 117,
  page		= {387--423}
}

@Article{	  tsy09b,
  author	= "Paul Tseng and S. Yun",
  title		= "A block-coordinate gradient descent method for linearly
		  constrained nonsmooth separable optimization",
  journal	= jota,
  year		= 2009,
  volume	= 140,
  page		= {513--535}
}

###Article{	  tsy09b,
  author	= "Paul Tseng and S. Yun",
  title		= "A block-coordinate gradient descent method for linearly
		  constrained nonsmooth separable optimization",
  journal	= jota,
  year		= 2009,
  volume	= 140,
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###TechReport{	  wnf07,
  author	= "Wright, S. J. and Nowak, R. D. and Figueiredo, M. A. T.",
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  institution	= "Computer Sciences Department, University of Wisconsin",
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  year		= 2007,
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}

###TechReport{	  wnf07,
  author	= "Wright, S. J. and Nowak, R. D. and Figueiredo, M. A. T.",
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  institution	= "Computer Sciences Department, University of Wisconsin",
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  year		= 2007,
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###Book{	  wri97,
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###Article{	  wrig:1990,
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###Article{	  wrig:1990,
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###Article{	  wrig:1998,
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@Article{	  wright2009sparse,
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###Article{	  wright2009sparse,
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		  M{\'a}rio AT},
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@Article{	  wright:1998,
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###Article{	  wright:1998,
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###Article{	  wuye:2002,
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@TechReport{	  wyyz07,
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###TechReport{	  wyyz07,
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###TechReport{	  wyyz07,
  author	= "Y. Wang and J. Yang and W. Yin and Y. Zhang",
  title		= "A new alternating minimization algorithm for total
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###Article{	  wzyb08,
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###Article{	  xueye:2000,
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###Article{	  xueye:2000,
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@Misc{		  yall1:2015,
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###Misc{	  yall1:2015,
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###Book{	  ye:1997,
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  title		= {Interior-Point Algorithms: Theory and Analysis},
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###Book{	  ye:1997,
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###Article{	  yul06,
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###Article{	  yul07,
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@TechReport{	  yut08,
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###TechReport{	  yut08,
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###TechReport{	  yut08,
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  institution	= "Department of Mathematics, National University of
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  address	= "Singapore",
  year		= 2008,
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}

@Article{	  yuzhaowang:2005,
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  journal	= {Phys. Med. Biol.},
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  title		= {A differentiable Shepp-Logan phantom and its application
		  in exact cone-beam {CT}},
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  year		= 2005
}

###Article{	  yuzhaowang:2005,
  author	= {H.-Y. Yu and S.-Y. Zhao and Ge Wang},
  journal	= {Phys. Med. Biol.},
  pages		= {5583--5595},
  title		= {A differentiable Shepp-Logan phantom and its application
		  in exact cone-beam {CT}},
  volume	= 50,
  year		= 2005
}

###Article{	  yuzhaowang:2005,
  author	= {H.-Y. Yu and S.-Y. Zhao and Ge Wang},
  journal	= {Phys. Med. Biol.},
  pages		= {5583--5595},
  title		= {A differentiable Shepp-Logan phantom and its application
		  in exact cone-beam {CT}},
  volume	= 50,
  year		= 2005
}

@TechReport{	  yzy08,
  author	= "J. Yang and Y. Zhang and W. Yin",
  title		= "An efficient {TVL1} algorithm for deblurring multichannel
		  images corrupted by impulsive noise",
  institution	= "Department of Computational and Applied Mathematics, Rice
		  University",
  address	= "Houston",
  year		= 2008
}

###TechReport{	  yzy08,
  author	= "J. Yang and Y. Zhang and W. Yin",
  title		= "An efficient {TVL1} algorithm for deblurring multichannel
		  images corrupted by impulsive noise",
  institution	= "Department of Computational and Applied Mathematics, Rice
		  University",
  address	= "Houston",
  year		= 2008
}

###TechReport{	  yzy08,
  author	= "J. Yang and Y. Zhang and W. Yin",
  title		= "An efficient {TVL1} algorithm for deblurring multichannel
		  images corrupted by impulsive noise",
  institution	= "Department of Computational and Applied Mathematics, Rice
		  University",
  address	= "Houston",
  year		= 2008
}

@Article{	  zhaoli:2002,
  author	= {Y.-B. Zhao and D. Li},
  date-modified	= {2007-07-18 14:59:33 -0700},
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  pages		= {893-912},
  title		= {Locating the Least 2-Norm Solution of Linear Programs via
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  volume	= 12,
  year		= 2002
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###Article{	  zhaoli:2002,
  author	= {Y.-B. Zhao and D. Li},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {893-912},
  title		= {Locating the Least 2-Norm Solution of Linear Programs via
		  a Path-Following Method},
  volume	= 12,
  year		= 2002
}

###Article{	  zhaoli:2002,
  author	= {Y.-B. Zhao and D. Li},
  date-modified	= {2007-07-18 14:59:33 -0700},
  journal	= siamopt,
  number	= 4,
  pages		= {893-912},
  title		= {Locating the Least 2-Norm Solution of Linear Programs via
		  a Path-Following Method},
  volume	= 12,
  year		= 2002
}

@TechReport{	  zhc08,
  author	= "M. Zhu and T. F. Chan",
  title		= "An efficient primal-dual hybrid gradient algorithm for
		  total variation image restoration",
  institution	= "Department of Mathematics, UCLA",
  address	= "Los Angeles",
  year		= 2008
}

###TechReport{	  zhc08,
  author	= "M. Zhu and T. F. Chan",
  title		= "An efficient primal-dual hybrid gradient algorithm for
		  total variation image restoration",
  institution	= "Department of Mathematics, UCLA",
  address	= "Los Angeles",
  year		= 2008
}

###TechReport{	  zhc08,
  author	= "M. Zhu and T. F. Chan",
  title		= "An efficient primal-dual hybrid gradient algorithm for
		  total variation image restoration",
  institution	= "Department of Mathematics, UCLA",
  address	= "Los Angeles",
  year		= 2008
}

@Conference{	  zhenjaco:2008a,
  author	= {Jing Zheng and Eddie Jacobs},
  editor	= {Z. Rahman and Stephen E. Reichenbach and Mark A. Neifeld},
  title		= {Application of compressive sensing theory in infrared
		  imaging systems},
  publisher	= {SPIE},
  year		= {2008},
  booktitle	= {Visual Information Processing XVII},
  volume	= {6978},
  numpages	= {8},
  location	= {Orlando, FL, USA},
  url		= {http://link.aip.org/link/?PSI/6978/69780J/1},
  doi		= {10.1117/12.776967}
}

###Conference{	  zhenjaco:2008a,
  author	= {Jing Zheng and Eddie Jacobs},
  editor	= {Z. Rahman and Stephen E. Reichenbach and Mark A. Neifeld},
  title		= {Application of compressive sensing theory in infrared
		  imaging systems},
  publisher	= {SPIE},
  year		= {2008},
  booktitle	= {Visual Information Processing XVII},
  volume	= {6978},
  numpages	= {8},
  location	= {Orlando, FL, USA},
  url		= {http://link.aip.org/link/?PSI/6978/69780J/1},
  doi		= {10.1117/12.776967}
}

###Conference{	  zhenjaco:2008a,
  author	= {Jing Zheng and Eddie Jacobs},
  editor	= {Z. Rahman and Stephen E. Reichenbach and Mark A. Neifeld},
  title		= {Application of compressive sensing theory in infrared
		  imaging systems},
  publisher	= {SPIE},
  year		= {2008},
  booktitle	= {Visual Information Processing XVII},
  volume	= {6978},
  numpages	= {8},
  location	= {Orlando, FL, USA},
  url		= {http://link.aip.org/link/?PSI/6978/69780J/1},
  doi		= {10.1117/12.776967}
}

@Conference{	  zhenjaco:2008b,
  author	= {Jing Zheng and Eddie Jacobs},
  editor	= {G. C. Holst},
  title		= {The application of Compressive Sensing technique on a
		  stationary surveillance camera system},
  publisher	= {SPIE},
  year		= {2008},
  booktitle	= {Infrared Imaging Systems: Design, Analysis, Modeling, and
		  Testing XIX},
  volume	= {6941},
  numpages	= {8},
  location	= {Orlando, FL, USA},
  url		= {http://link.aip.org/link/?PSI/6941/69410H/1},
  doi		= {10.1117/12.779321}
}

###Conference{	  zhenjaco:2008b,
  author	= {Jing Zheng and Eddie Jacobs},
  editor	= {G. C. Holst},
  title		= {The application of Compressive Sensing technique on a
		  stationary surveillance camera system},
  publisher	= {SPIE},
  year		= {2008},
  booktitle	= {Infrared Imaging Systems: Design, Analysis, Modeling, and
		  Testing XIX},
  volume	= {6941},
  numpages	= {8},
  location	= {Orlando, FL, USA},
  url		= {http://link.aip.org/link/?PSI/6941/69410H/1},
  doi		= {10.1117/12.779321}
}

###Conference{	  zhenjaco:2008b,
  author	= {Jing Zheng and Eddie Jacobs},
  editor	= {G. C. Holst},
  title		= {The application of Compressive Sensing technique on a
		  stationary surveillance camera system},
  publisher	= {SPIE},
  year		= {2008},
  booktitle	= {Infrared Imaging Systems: Design, Analysis, Modeling, and
		  Testing XIX},
  volume	= {6941},
  numpages	= {8},
  location	= {Orlando, FL, USA},
  url		= {http://link.aip.org/link/?PSI/6941/69410H/1},
  doi		= {10.1117/12.779321}
}

@Article{	  zibupear:2001,
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  year		= 2001
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###Article{	  zibupear:2001,
  author	= {M. Zibulevsky and B. A. Pearlmutter},
  issue		= 4,
  journal	= {Neural Computation},
  pages		= {863--882},
  title		= {Blind source separation by sparse decomposition in a
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  volume	= 13,
  year		= 2001
}

###Article{	  zibupear:2001,
  author	= {M. Zibulevsky and B. A. Pearlmutter},
  issue		= 4,
  journal	= {Neural Computation},
  pages		= {863--882},
  title		= {Blind source separation by sparse decomposition in a
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  volume	= 13,
  year		= 2001
}

@TechReport{	  zwc08,
  author	= "M. Zhu and S. J. Wright and T. F. Chan",
  title		= "Duality-based algorithms for total-variation regularized
		  image restoration",
  institution	= "Department of Mathematics, UCLA",
  address	= "Los Angeles",
  year		= 2008
}

###TechReport{	  zwc08,
  author	= "M. Zhu and S. J. Wright and T. F. Chan",
  title		= "Duality-based algorithms for total-variation regularized
		  image restoration",
  institution	= "Department of Mathematics, UCLA",
  address	= "Los Angeles",
  year		= 2008
}

###TechReport{	  zwc08,
  author	= "M. Zhu and S. J. Wright and T. F. Chan",
  title		= "Duality-based algorithms for total-variation regularized
		  image restoration",
  institution	= "Department of Mathematics, UCLA",
  address	= "Los Angeles",
  year		= 2008
}

@article{friedlander2012hybrid,
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