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Copy file name to clipboardExpand all lines: docs/src/benchmark.md
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@@ -12,13 +12,13 @@ using OptimizationProblems.PureJuMP
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```
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We select the problems from `PureJuMP` submodule of `OptimizationProblems` converted in [NLPModels](https://github.com/JuliaSmoothOptimizers/NLPModels.jl) using [NLPModelsJuMP](https://github.com/JuliaSmoothOptimizers/NLPModelsJuMP.jl).
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```@example ex1
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problems = (MathOptNLPModel(eval(Meta.parse(problem))(), name=problem) for problem ∈ OptimizationProblems.meta[!, :name])
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problems = (MathOptNLPModel(OptimizationProblems.PureJuMP.eval(Meta.parse(problem))(), name=problem) for problem ∈ OptimizationProblems.meta[!, :name])
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```
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The same can be achieved using `OptimizationProblems.ADNLPProblems` instead of `OptimizationProblems.PureJuMP` as follows:
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```@example ex1
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using ADNLPModels
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using OptimizationProblems.ADNLPProblems
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ad_problems = (eval(Meta.parse(problem))() for problem ∈ OptimizationProblems.meta[!, :name])
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ad_problems = (OptimizationProblems.ADNLPProblems.eval(Meta.parse(problem))() for problem ∈ OptimizationProblems.meta[!, :name])
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```
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We also define a dictionary of solvers that will be used for our benchmark. We consider here `JSOSolvers.lbfgs` and `JSOSolvers.trunk`.
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