First try to use pkg extension

Project.toml

@@ -8,31 +8,50 @@ ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 JSOSolvers = "10dff2fc-5484-5881-a0e0-c90441020f8a"
 JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
+KNITRO = "67920dd8-b58e-52a8-8622-53c4cffbe346"
 LLSModels = "39f5bc3e-5160-4bf8-ac48-504fd2534d24"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearOperators = "5c8ed15e-5a4c-59e4-a42b-c7e8811fb125"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"
 NLPModelsModifiers = "e01155f1-5c6f-4375-a9d8-616dd036575f"
+PackageExtensionCompat = "65ce6f38-6b18-4e1d-a461-8949797d7930"
 Percival = "01435c0c-c90d-11e9-3788-63660f8fbccc"
 QuadraticModels = "f468eda6-eac5-11e8-05a5-ff9e497bcd19"
-Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 SolverCore = "ff4d7338-4cf1-434d-91df-b86cb86fb843"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
+[weakdeps]
+CaNNOLeS = "5a1c9e79-9c58-5ec0-afc4-3298fdea2875"
+DCISolver = "bee2e536-65f6-11e9-3844-e5bb4c9c55c9"
+FletcherPenaltySolver = "e59f0261-166d-4fee-8bf3-5e50457de5db"
+NLPModelsIpopt = "f4238b75-b362-5c4c-b852-0801c9a21d71"
+NLPModelsKnitro = "bec4dd0d-7755-52d5-9a02-22f0ffc7efcb"
+RipQP = "1e40b3f8-35eb-4cd8-8edd-3e515bb9de08"
+SolverBenchmark = "581a75fa-a23a-52d0-a590-d6201de2218a"
+
+[extensions]
+CaNNOLeSExt = ["CaNNOLeS"]
+DCISolverExt = ["DCISolver"]
+FletcherPenaltySolverExt = ["FletcherPenaltySolver"]
+NLPModelsIpoptExt = ["NLPModelsIpopt"]
+NLPModelsKnitroExt = ["NLPModelsKnitro"]
+RipQPExt = ["RipQP"]
+SolverBenchmarkExt = ["SolverBenchmark"]
+
 [compat]
 ADNLPModels = "0.4, 0.5, 0.6, 0.7"
 DataFrames = "1"
 JSOSolvers = "0.10, 0.11"
 JuMP = "1"
+KNITRO = "0.13.0"
 LLSModels = "0.3.6"
 LinearOperators = "2"
 NLPModels = "0.19, 0.20"
 NLPModelsJuMP = "0.11, 0.12"
 NLPModelsModifiers = "0.6.2"
 Percival = "0.6, 0.7"
 QuadraticModels = "0.9.2"
-Requires = "1"
 SolverCore = "0.3"
 julia = "^1.6.0"

docs/src/benchmark.md

@@ -43,7 +43,7 @@ using NLPModelsIpopt
 selected_optimizers = JSOSuite.optimizers
 # optimizers can solve general `nlp` as some are specific to variants (NLS, ...)
 selected_optimizers = selected_optimizers[selected_optimizers.can_solve_nlp, :]
-selected_optimizers[selected_optimizers.is_available, :] # optimizers available
+is_available(selected_optimizers) # optimizers available
 ```
 
 For the purpose of this example, we will consider 3 optimizers.

ext/CaNNOLeSExt.jl

@@ -0,0 +1,9 @@
+module CaNNOLeSExt
+
+  using CaNNOLeS, JSOSuite
+
+  function minimize(::Val{:CaNNOLeS}, nlp; kwargs...)
+    return CaNNOLeS.cannoles(nlp; linsolve = :ldlfactorizations, kwargs...)
+  end
+
+end

ext/DCISolverExt.jl

@@ -0,0 +1,7 @@
+module DCISolverExt
+
+using DCISolver, JSOSuite
+
+minimize(::Val{:DCISolver}, nlp; kwargs...) = DCISolver.dci(nlp; kwargs...)
+
+end

ext/FletcherPenaltySolverExt.jl

@@ -0,0 +1,9 @@
+module FletcherPenaltySolverExt
+
+using FletcherPenaltySolver, JSOSuite
+
+function minimize(::Val{:FletcherPenaltySolver}, nlp; kwargs...)
+  return FletcherPenaltySolver.fps_solve(nlp; kwargs...)
+end
+
+end

src/solvers/ipopt_solve.jl → ext/NLPModelsIpoptExt.jl

@@ -1,3 +1,6 @@
+module NLPModelsIpoptExt
+
+using NLPModelsIpopt, JSOSuite
 # Selection of possible [options](https://coin-or.github.io/Ipopt/OPTIONS.html#OPTIONS_REF).
 function minimize(::Val{:IPOPT}, nlp; kwargs...)
   keywords = Dict(kwargs)
@@ -34,3 +37,5 @@ function minimize(::Val{:IPOPT}, nlp; kwargs...)
   end
   return NLPModelsIpopt.ipopt(nlp; keywords...)
 end
+
+end

src/solvers/knitro_solve.jl → ext/NLPModelsKnitroExt.jl

@@ -1,3 +1,7 @@
+module NLPModelsKnitroExt
+
+using NLPModelsKnitro, JSOSuite
+
 # See https://www.artelys.com/docs/knitro/3_referenceManual/userOptions.html for the list of options accepted.
 function minimize(::Val{:KNITRO}, nlp; kwargs...)
   keywords = Dict(kwargs)
@@ -25,3 +29,5 @@ function minimize(::Val{:KNITRO}, nlp; kwargs...)
   end
   return NLPModelsKnitro.knitro(nlp; keywords...)
 end
+
+end

src/solvers/ripqp_solve.jl → ext/RipQPExt.jl

@@ -1,3 +1,7 @@
+module RipQPExt
+
+using RipQP, JSOSuite, LLSModels, QuadraticModels
+
 function minimize(
   ::Val{:RipQP},
   nlp::Union{QuadraticModel{T0}, LLSModel{T0}};
@@ -60,3 +64,5 @@ function minimize(
   end
   return RipQP.ripqp(nlp; itol = itol, keywords...)
 end
+
+end

ext/SolverBenchmarkExt.jl

@@ -0,0 +1,31 @@
+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