First try to use pkg extension

Author: tmigot

ext/CaNNOLeSExt.jl

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+module CaNNOLeSExt
+
+  using CaNNOLeS, JSOSuite
+
+  JSOSuite.optimizers[JSOSuite.optimizers.name .== "CaNNOLeS", :is_available] .= 1
+
+  function minimize(::Val{:CaNNOLeS}, nlp; kwargs...)
+    return CaNNOLeS.cannoles(nlp; linsolve = :ldlfactorizations, kwargs...)
+  end
+
+end

ext/DCISolverExt.jl

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+module DCISolverExt
+
+using DCISolver, JSOSuite
+
+JSOSuite.optimizers[JSOSuite.optimizers.name .== "DCISolver", :is_available] .= 1
+    function minimize(::Val{:DCISolver}, nlp; kwargs...)
+      return DCISolver.dci(nlp; kwargs...)
+    end
+
+end

ext/FletcherPenaltySolverExt.jl

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+module FletcherPenaltySolverExt
+
+using FletcherPenaltySolver, JSOSuite
+JSOSuite.optimizers[JSOSuite.optimizers.name .== "FletcherPenaltySolver", :is_available] .= 1
+    function minimize(::Val{:FletcherPenaltySolver}, nlp; kwargs...)
+      return FletcherPenaltySolver.fps_solve(nlp; kwargs...)
+    end
+end

ext/NLPModelsIpoptExt.jl

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+module NLPModelsIpoptExt
+
+using NLPModelsIpopt, JSOSuite
+JSOSuite.optimizers[JSOSuite.optimizers.name .== "IPOPT", :is_available] .= 1
+    include("solvers/ipopt_solve.jl")
+end

ext/NLPModelsKnitroExt.jl

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+module NLPModelsKnitroExt
+
+using KNITRO, NLPModelsKnitro, JSOSuite
+  JSOSuite.optimizers[JSOSuite.optimizers.name .== "KNITRO", :is_available] .= KNITRO.has_knitro()
+  include("solvers/knitro_solve.jl")
+end

ext/RipQPExt.jl

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+module RipQPExt
+
+using RipQP, JSOSuite, LLSModels, QuadraticModels
+JSOSuite.optimizers[JSOSuite.optimizers.name .== "RipQP", :is_available] .= 1
+    include("solvers/ripqp_solve.jl")
+end

ext/SolverBenchmarkExt.jl

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+module SolverBenchmarkExt
+
+using SolverBenchmark, JSOSuite
+function SolverBenchmark.bmark_solvers(
+    problems,
+    solver_names::Vector{String},
+    solvers::Dict{Symbol, Function} = Dict{Symbol, Function}();
+    atol::Real = √eps(),
+    rtol::Real = √eps(),
+    verbose::Integer = 0,
+    max_time::Float64 = 300.0,
+    max_eval::Integer = 10000,
+    max_iter::Integer = 10000,
+    kwargs...,
+  )
+    for s in solver_names
+      solvers[Symbol(s)] =
+        nlp -> minimize(
+          s,
+          nlp;
+          atol = atol,
+          rtol = rtol,
+          verbose = verbose,
+          max_time = max_time,
+          max_eval = max_eval,
+          max_iter = max_iter,
+        )
+    end
+    return SolverBenchmark.bmark_solvers(solvers, problems; kwargs...)
+  end
+end