testqn.jl

enable_autocomplete_brackets(false)  # do this if using OhMyRepl and Tmux

using Pkg
Pkg.activate("dev-jso-solvers")

Pkg.develop(PackageSpec(url="../LinearOperators.jl"))
Pkg.develop(PackageSpec(url="../NLPModels.jl"))
Pkg.develop(PackageSpec(url="../NLPModelsJuMP.jl"))
Pkg.develop(PackageSpec(url="../SolverTools.jl"))
# Pkg.develop(PackageSpec(url="../OptimizationProblems.jl"))
Pkg.develop(PackageSpec(url="."))
Pkg.add("OptimizationProblems")
Pkg.add("SolverBenchmark")
Pkg.add("DataFrames")

using NLPModels, JSOSolvers, NLPModelsJuMP, OptimizationProblems
using SolverTools, SolverBenchmark, DataFrames

probs = (MathOptNLPModel(eval(prob)(), name=string(prob)) for prob ∈ names(OptimizationProblems) if prob != :OptimizationProblems)
solvers = Dict{Symbol,Function}(
                                :trunk => p -> trunk(p, max_eval=1000),
                                :trunklsr1 => p -> trunk(LSR1Model(p, scaling=true), max_eval=3000),
                                :trunklbfgs => p -> trunk(LBFGSModel(p, scaling=true), max_eval=3000),
                               )

stats = bmark_solvers(solvers, probs, skipif=p -> (p.meta.nvar < 100 || p.meta.ncon > 0))

Pkg.add("Plots")
using Plots

solved(df) = df.status .== :first_order

costnames = ["elapsed time",
             "objective evals",
             "gradient evals",
             "hessian-vector products",
             "obj + grad + hprod"]

costs = [df -> .!solved(df) .* Inf .+ df.elapsed_time,
         df -> .!solved(df) .* Inf .+ df.neval_obj,
         df -> .!solved(df) .* Inf .+ df.neval_grad,
         df -> .!solved(df) .* Inf .+ df.neval_hprod,
         df -> .!solved(df) .* Inf .+ df.neval_obj .+ df.neval_grad .+ df.neval_hprod]

profile_solvers(stats, costs, costnames)
savefig("trunkqn.png")