add test solver function

Author: MaxenceGollier

test/runtests.jl

@@ -4,6 +4,7 @@ using ProximalOperators
 using ADNLPModels,
   OptimizationProblems,
   OptimizationProblems.ADNLPProblems,
+  ManualNLPModels,
   NLPModels,
   NLPModelsModifiers,
   RegularizedProblems,
@@ -19,6 +20,8 @@ const global bpdn2, bpdn_nls2, sol2 = bpdn_model(compound, bounds = true)
 const global λ = norm(grad(bpdn, zeros(bpdn.meta.nvar)), Inf) / 10
 
 include("utils.jl")
+include("test-solver.jl")
+
 include("test_AL.jl")
 
 for (mod, mod_name) ∈ ((x -> x, "exact"), (LSR1Model, "lsr1"), (LBFGSModel, "lbfgs"))

test/test-solver.jl

@@ -0,0 +1,35 @@
+function test_solver(reg_nlp::R, solver_name::String; expected_status = :first_order, solver_constructor_kwargs = (;), solver_kwargs = (;)) where{R}
+    
+    # Test output with allocating calling form
+    solver_fun = getfield(RegularizedOptimization, Symbol(solver_name))
+    stats_basic = solver_fun(reg_nlp.model, reg_nlp.h, ROSolverOptions(); solver_constructor_kwargs..., solver_kwargs...)
+
+    x0 = get(solver_kwargs, :x0, reg_nlp.model.meta.x0)
+    @test typeof(stats_basic.solution) == typeof(x0)
+    @test length(stats_basic.solution) == reg_nlp.model.meta.nvar
+    @test typeof(stats_basic.dual_feas) == eltype(stats_basic.solution)
+    @test stats_basic.status == expected_status
+    @test obj(reg_nlp, stats_basic.solution) == stats_basic.objective
+    @test stats_basic.objective <= obj(reg_nlp, x0)
+
+    # Test output with optimized calling form
+    solver_constructor = getfield(RegularizedOptimization, Symbol(solver_name*"Solver"))
+    solver = solver_constructor(reg_nlp; solver_constructor_kwargs...)
+    stats_optimized = RegularizedExecutionStats(reg_nlp)
+
+    # Remove the x0 entry from solver_kwargs
+    optimized_solver_kwargs = Base.structdiff(solver_kwargs, NamedTuple{(:x0,)})
+    solve!(solver, reg_nlp, stats_optimized; x = x0, optimized_solver_kwargs...) # It would be interesting to check for allocations here as well but depending on 
+                                                               # the structure of solver_kwargs, some variables might get boxed, resulting in 
+                                                               # false positives, for example if tol = 1e-3; solver_kwargs = (atol = tol),
+                                                               # then wrappedallocs would give a > 0 answer...
+    @test typeof(stats_optimized.solution) == typeof(x0)
+    @test length(stats_optimized.solution) == reg_nlp.model.meta.nvar
+    @test typeof(stats_optimized.dual_feas) == eltype(stats_optimized.solution)
+    @test stats_optimized.status == expected_status
+    @test obj(reg_nlp, stats_optimized.solution) == stats_optimized.objective
+    @test stats_optimized.objective <= obj(reg_nlp, x0)
+
+    # TODO: test that the optimized entries in stats_optimized and stats_basic are the same.
+
+end