clean

Fix NLS symbol issue

remove CaNNOLeS in compat

Author: tmigot

Project.toml

@@ -42,22 +42,17 @@ SolverBenchmarkExt = ["SolverBenchmark"]
 
 [compat]
 ADNLPModels = "0.4, 0.5, 0.6, 0.7"
-CaNNOLeS = "0.7"
 DataFrames = "1"
-FletcherPenaltySolver = "0.2"
 JSOSolvers = "0.10, 0.11"
 JuMP = "1"
 KNITRO = "0.13.0"
 LLSModels = "0.3.6"
 LinearOperators = "2"
 NLPModels = "0.19, 0.20"
-NLPModelsIpopt = "0.10"
 NLPModelsJuMP = "0.11, 0.12"
 NLPModelsKnitro = "0.7"
 NLPModelsModifiers = "0.6.2"
 Percival = "0.6, 0.7"
 QuadraticModels = "0.9.2"
-RipQP = "0.5, 0.6"
-SolverBenchmark = "0.5"
 SolverCore = "0.3"
 julia = "^1.6.0"

src/JSOSuite.jl

@@ -143,7 +143,7 @@ push!(
 push!(
   optimizers,
   (
-    "TRON-NLS",
+    "TRONNLS",
     :TronSolverNLS,
     "JSOSolvers.jl",
     :tron,
@@ -161,7 +161,7 @@ push!(
 push!(
   optimizers,
   (
-    "TRUNK-NLS",
+    "TRUNKNLS",
     :TrunkSolverNLS,
     "JSOSolvers.jl",
     :trunk,
@@ -297,7 +297,14 @@ Return `true` if the solver `name` is available.
 is_available(name::String) = is_available(Symbol(name))
 is_available(name::Symbol) = is_available(Val(name))
 
-is_available(::Val{name}) where {name} = true
+is_available(::Val{name}) where {name} = false
+is_available(::Val{:R2}) = true
+is_available(::Val{:LBFGS}) = true
+is_available(::Val{:TRON}) = true
+is_available(::Val{:TRUNK}) = true
+is_available(::Val{:TRONNLS}) = true
+is_available(::Val{:TRUNKNLS}) = true
+is_available(::Val{:Percival}) = true
 is_available(::Val{:CaNNOLeS}) = !isnothing(Base.get_extension(JSOSuite, :CaNNOLeSExt))
 is_available(::Val{:DCISolver}) = !isnothing(Base.get_extension(JSOSuite, :DCISolverExt))
 is_available(::Val{:FletcherPenaltySolver}) = !isnothing(Base.get_extension(JSOSuite, :FletcherPenaltySolverExt))

src/selection.jl

@@ -52,8 +52,8 @@ Algorithm selection:
 - nonlinear objective: ✓;
 - may use 2-th order derivative.
 There are 7 optimizers available:
-["LBFGS", "R2", "TRON", "TRUNK", "TRON-NLS", "TRUNK-NLS", "Percival"].
-["LBFGS", "R2", "TRON", "TRUNK", "TRON-NLS", "TRUNK-NLS", "Percival"]
+["LBFGS", "R2", "TRON", "TRUNK", "TRONNLS", "TRUNKNLS", "Percival"].
+["LBFGS", "R2", "TRON", "TRUNK", "TRONNLS", "TRUNKNLS", "Percival"]
 ```
 """
 function select_optimizers(

test/runtests.jl

@@ -61,19 +61,20 @@ end
   nlp = OptimizationProblems.ADNLPProblems.arglina()
   model = OptimizationProblems.PureJuMP.arglina()
   @testset "Test $solver_name" for solver_name in JSOSuite.optimizers[!, :name_solver]
+    name = JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :name][1]
     solver_name == :DCIWorkspace && continue
     solver_name == :RipQPSolver && continue
     can_solve_nlp =
       JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :can_solve_nlp]
     spec_nls =
       JSOSuite.optimizers[JSOSuite.optimizers.name_solver .== solver_name, :specialized_nls]
-    if is_available(solver_name) && can_solve_nlp[1]
+    if is_available(name) && can_solve_nlp[1]
       test_in_place_solve(nlp, solver_name)
       test_in_place_solve(model, solver_name)
-    elseif is_available(solver_name) && spec_nls[1] # NLS
+    elseif is_available(name) && spec_nls[1] # NLS
       nls = OptimizationProblems.ADNLPProblems.arglina(use_nls = true)
       test_in_place_solve(nls, solver_name)
-    elseif is_available(solver_name) # RipQP
+    elseif is_available(name) # RipQP
       nlp_qm = QuadraticModel(nlp, nlp.meta.x0)
       test_in_place_solve(nlp_qm, solver_name)
     end
@@ -112,10 +113,8 @@ end
     OptimizationProblems.ADNLPProblems.eval(Meta.parse(problem))() for
     problem ∈ meta[(5 .<= meta.nvar .<= 10) .& (meta.ncon .== 0) .& (.!meta.has_bounds), :name]
   ]
-  select = JSOSuite.optimizers[
-    JSOSuite.optimizers.can_solve_nlp .& JSOSuite.optimizers.is_available,
-    :name,
-  ]
+  select = is_available(JSOSuite.optimizers)
+  select = select[JSOSuite.optimizers.can_solve_nlp, :name]
   stats = bmark_solvers(ad_problems, select, atol = 1e-3, max_time = 10.0, verbose = 0)
   @test true # just test that it runs
 end
@@ -158,7 +157,7 @@ for solver in eachrow(JSOSuite.optimizers)
   nlp = mgh17()
   @testset "Test options in $(solver.name)" begin
     # We just test that the solver runs with the options
-    if solver.is_available
+    if is_available(solver.name)
       if solver.nonlinear_obj
         minimize(
           solver.name,
@@ -193,7 +192,7 @@ end
   callback = (args...) -> nothing
   for solver in eachrow(JSOSuite.optimizers)
     @testset "Test options in $(solver.name)" begin
-      solver.is_available || continue
+      is_available(solver.name) || continue
       ((nlp.meta.ncon > 0) && (!solver.equalities)) && continue
       # We just test that the solver runs with the options
       if solver.can_solve_nlp