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Update dependencies #147

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Update dependencies #147

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373f22b
Update the documentation for HSL
amontoison Jul 31, 2024
5cf4578
Update dependencies
amontoison Jul 31, 2024
4f6be68
Update Project.toml
tmigot Aug 13, 2024
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6 changes: 3 additions & 3 deletions Project.toml
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Expand Up @@ -15,9 +15,9 @@ SolverTools = "b5612192-2639-5dc1-abfe-fbedd65fab29"
SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"

[compat]
HSL = "0.3, 0.4"
Krylov = "0.8, 0.9"
LDLFactorizations = "0.8.1, 0.9, 0.10"
HSL = "0.4"
Krylov = "0.9"
LDLFactorizations = "0.10"
Comment on lines +18 to +20
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You don't change the code, so I don't see any reason to drop these

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Because nothing is tested with older version of the packages.

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HSL 0.3 doesn't support the JLL for example.
You should not allow it!

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Still, I don't think we should drop this if no coding reason.

LinearAlgebra = "1"
NLPModels = "0.15, 0.16, 0.17, 0.18, 0.19, 0.20, 0.21"
Printf = "1"
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -24,7 +24,7 @@ optimization problems of the form

It uses other JuliaSmoothOptimizers packages for development.
In particular, [NLPModels.jl](https://github.com/JuliaSmoothOptimizers/NLPModels.jl) is used for defining the problem, and [SolverCore](https://github.com/JuliaSmoothOptimizers/SolverCore.jl) for the output.
It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. Follow [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl)'s `MA57` installation for an alternative.
It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl) provides alternative linear solvers if [libHSL](https://licences.stfc.ac.uk/product/libhsl) can be downloaded.
The feasibility steps are factorization-free and use iterative methods from [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl)

## References
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8 changes: 4 additions & 4 deletions docs/Project.toml
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Expand Up @@ -21,15 +21,15 @@ SolverBenchmark = "581a75fa-a23a-52d0-a590-d6201de2218a"
SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"

[compat]
ADNLPModels = "0.7"
ADNLPModels = "0.8.4"
CUTEst = "0.13"
Documenter = "0.26"
Gridap = "0.15.5"
JSOSolvers = "0.11"
JSOSolvers = "0.12"
LDLFactorizations = "0.10"
NLPModels = "0.20"
NLPModels = "0.21"
NLPModelsIpopt = "0.10"
NLPModelsModifiers = "0.7"
PDENLPModels = "0.3"
SolverBenchmark = "0.5"
SolverBenchmark = "0.6"
SolverCore = "0.3"
2 changes: 1 addition & 1 deletion docs/src/index.md
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Expand Up @@ -32,7 +32,7 @@ add DCISolver

The DCI algorithm is an iterative method that has the flavor of a projected gradient algorithm and could be characterized as
a relaxed feasible point method with dynamic control of infeasibility. It is a combination of two steps: a tangent step and a feasibility step.
It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. Follow [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl)'s `MA57` installation for an alternative.
It uses [LDLFactorizations.jl](https://github.com/JuliaSmoothOptimizers/LDLFactorizations.jl) by default to compute the factorization in the tangent step. [HSL.jl](https://github.com/JuliaSmoothOptimizers/HSL.jl) provides alternative linear solvers if [libHSL](https://licences.stfc.ac.uk/product/libhsl) can be downloaded.
The feasibility steps are factorization-free and use iterative methods from [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl).

## Example
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2 changes: 1 addition & 1 deletion test/Project.toml
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Expand Up @@ -14,7 +14,7 @@ SolverTest = "4343dc35-3317-4c6e-8877-f0cc8502c90e"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[compat]
ADNLPModels = "0.7.0"
ADNLPModels = "0.8.4"
BenchmarkTools = "1.4.0"
HSL = "0.4"
Krylov = "0.9"
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