QR Decomposition Extremely Slow Converting to Float64 Matrix

[clintonTE]

It seems converting a qr decomposition Q matrix to a Float Matrix takes high-degree polynomial time at best.

Seems like a bug. I don’t remember having this problem on earlier versions (I am using 1.01 on Windows 10). Granted my laptop is pretty slow, but this is worse than matrix inversion, so the performance doesn’t make a lot of sense to me.

using LinearAlgebra
function mwe()
  local Q::Matrix{Float64}

  X = rand(200,13)
  f = qr(X)
  @time Q = Matrix{Float64}(f.Q)

  X = rand(400,13)
  f = qr(X)
  @time Q = Matrix{Float64}(f.Q)

  X = rand(600,13)
  f = qr(X)
  @time Q = Matrix{Float64}(f.Q)

  X = rand(800,13)
  f = qr(X)
  @time Q = Matrix{Float64}(f.Q)
end

mwe()

Output:
0.260873 seconds (120.00 k allocations: 145.569 MiB, 1.49% gc time)
2.115116 seconds (480.00 k allocations: 1.022 GiB, 3.70% gc time)
6.812497 seconds (1.08 M allocations: 3.372 GiB, 4.46% gc time)
15.563167 seconds (1.92 M allocations: 7.901 GiB, 3.08% gc time)

Version Info:

Julia Version 1.0.1
Commit 0d713926f8 (2018-09-29 19:05 UTC)
Platform Info:
  OS: Windows (x86_64-w64-mingw32)
  CPU: Intel(R) Core(TM) i7-4650U CPU @ 1.70GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-6.0.0 (ORCJIT, haswell)

Also on discourse:
https://discourse.julialang.org/t/qr-decomposition-extremely-slow/16946?u=clinton

[RalphAS]

Specifying the type parameter Matrix{Float64}(f.Q) dispatches to a generic copy constructor, whereas Matrix(f.Q) dispatches to the proper method in qr.jl.

[clintonTE]

Thanks for the work around! That worked for what I was doing. Note that Matrix(f.Q[:,:]) is also slow.