Add selective error handling for DomainError in tests

test/test_JuMP.jl

@@ -2,78 +2,82 @@
 function test_JuMP()
     for f in LIST_OF_PROBLEMS
         @testset "$(f)" verbose=VERBOSE begin
-            grid_size = metadata(f)[:grid_size]
+            try
+                grid_size = metadata(f)[:grid_size]
 
-            # do we keep or remove the problem from the list
-            keep_problem = true
+                # do we keep or remove the problem from the list
+                keep_problem = true
 
-            #
-            DEBUG && println("\n", "┌─ ", string(f), " (JuMP)")
-            DEBUG && println("│")
+                #
+                DEBUG && println("\n", "┌─ ", string(f), " (JuMP)")
+                DEBUG && println("│")
 
-            # Set up the model
-            nlp = OptimalControlProblems.eval(f)(JuMPBackend(); grid_size=grid_size)
-            set_optimizer(nlp, Ipopt.Optimizer)
-            set_silent(nlp)
-            set_optimizer_attribute(nlp, "tol", TOL)
-            set_optimizer_attribute(nlp, "max_iter", MAX_ITER)
-            set_optimizer_attribute(nlp, "mu_strategy", MU_STRATEGY)
-            set_optimizer_attribute(nlp, "linear_solver", "mumps")
-            set_optimizer_attribute(nlp, "max_wall_time", MAX_WALL_TIME)
-            set_optimizer_attribute(nlp, "sb", SB)
+                # Set up the model
+                nlp = OptimalControlProblems.eval(f)(JuMPBackend(); grid_size=grid_size)
+                set_optimizer(nlp, Ipopt.Optimizer)
+                set_silent(nlp)
+                set_optimizer_attribute(nlp, "tol", TOL)
+                set_optimizer_attribute(nlp, "max_iter", MAX_ITER)
+                set_optimizer_attribute(nlp, "mu_strategy", MU_STRATEGY)
+                set_optimizer_attribute(nlp, "linear_solver", "mumps")
+                set_optimizer_attribute(nlp, "max_wall_time", MAX_WALL_TIME)
+                set_optimizer_attribute(nlp, "sb", SB)
 
-            # check existence of required metadata
-            nlp_keys = keys(object_dictionary(nlp))
-            @test :time_grid ∈ nlp_keys
-            @test :state_components ∈ nlp_keys
-            @test :costate_components ∈ nlp_keys
-            @test :control_components ∈ nlp_keys
-            @test :variable_components ∈ nlp_keys
+                # check existence of required metadata
+                nlp_keys = keys(object_dictionary(nlp))
+                @test :time_grid ∈ nlp_keys
+                @test :state_components ∈ nlp_keys
+                @test :costate_components ∈ nlp_keys
+                @test :control_components ∈ nlp_keys
+                @test :variable_components ∈ nlp_keys
 
-            # check if the keys from the components names exists
-            components = [
-                :state_components,
-                :costate_components,
-                :control_components,
-                :variable_components,
-            ]
-            for c in components
-                if !(isnothing(nlp[c]))
-                    for e in nlp[c]
-                        @test Symbol(e) ∈ nlp_keys
+                # check if the keys from the components names exists
+                components = [
+                    :state_components,
+                    :costate_components,
+                    :control_components,
+                    :variable_components,
+                ]
+                for c in components
+                    if !(isnothing(nlp[c]))
+                        for e in nlp[c]
+                            @test Symbol(e) ∈ nlp_keys
+                        end
                     end
                 end
-            end
 
-            # Solve the model
-            print("  First solve:  ");
-            @time optimize!(nlp)
-            print("  Second solve: ");
-            @time optimize!(nlp)
+                # Solve the model
+                print("  First solve:  ");
+                @time optimize!(nlp)
+                print("  Second solve: ");
+                @time optimize!(nlp)
 
-            # Infos
-            DEBUG && println("│")
-            DEBUG && print(
-                "│ termination_status: ",
-                termination_status(nlp),
-                ", objective: ",
-                objective_value(nlp),
-                ", iterations: ",
-                barrier_iterations(nlp),
-            )
+                # Infos
+                DEBUG && println("│")
+                DEBUG && print(
+                    "│ termination_status: ",
+                    termination_status(nlp),
+                    ", objective: ",
+                    objective_value(nlp),
+                    ", iterations: ",
+                    barrier_iterations(nlp),
+                )
 
-            # Test
-            res = @my_test_broken termination_status(nlp) == MOI.LOCALLY_SOLVED
-            keep_problem = keep_problem && res
-            DEBUG && res && println(", \033[1;32mPass\033[0m")
-            DEBUG && !res && println(", \033[1;31mFail\033[0m")
-            DEBUG && println("│")
-            DEBUG && println("└─")
+                # Test
+                res = @my_test_broken termination_status(nlp) == MOI.LOCALLY_SOLVED
+                keep_problem = keep_problem && res
+                DEBUG && res && println(", \033[1;32mPass\033[0m")
+                DEBUG && !res && println(", \033[1;31mFail\033[0m")
+                DEBUG && println("│")
+                DEBUG && println("└─")
 
-            # do we keep or remove the problem from the list
-            if !keep_problem
-                global LIST_OF_PROBLEMS_FINAL
-                LIST_OF_PROBLEMS_FINAL = setdiff(LIST_OF_PROBLEMS_FINAL, [f])
+                # do we keep or remove the problem from the list
+                if !keep_problem
+                    global LIST_OF_PROBLEMS_FINAL
+                    LIST_OF_PROBLEMS_FINAL = setdiff(LIST_OF_PROBLEMS_FINAL, [f])
+                end
+            catch e
+                handle_solver_error(e, f)
             end
         end
     end

test/test_OptimalControl.jl

@@ -11,50 +11,54 @@ function test_OptimalControl()
 
     for f in LIST_OF_PROBLEMS
         @testset "$(f)" verbose=VERBOSE begin
-            grid_size = metadata(f)[:grid_size]
-
-            # do we keep or remove the problem from the list
-            keep_problem = true
-
-            #
-            DEBUG && println("\n", "┌─ ", string(f), " (OptimalControl)")
-            DEBUG && println("│")
-
-            # Set up the model
-            docp = OptimalControlProblems.eval(f)(
-                OptimalControlBackend(); grid_size=grid_size
-            )
-            nlp = nlp_model(docp)
-
-            # Solve the model
-            print("  First solve:  ");
-            @time sol = NLPModelsIpopt.ipopt(nlp; options_ipopt...)
-            print("  Second solve: ");
-            @time sol = NLPModelsIpopt.ipopt(nlp; options_ipopt...)
-
-            # Infos
-            DEBUG && println("│")
-            DEBUG && print(
-                "│ sol.status: ",
-                sol.status,
-                ", objective: ",
-                sol.objective,
-                ", iterations: ",
-                sol.iter,
-            )
-
-            # Test
-            res = @my_test_broken (sol.status == :first_order || sol.status == :acceptable)
-            keep_problem = keep_problem && res
-            DEBUG && res && println(", \033[1;32mPass\033[0m")
-            DEBUG && !res && println(", \033[1;31mFail\033[0m")
-            DEBUG && println("│")
-            DEBUG && println("└─")
-
-            # do we keep or remove the problem from the list
-            if !keep_problem
-                global LIST_OF_PROBLEMS_FINAL
-                LIST_OF_PROBLEMS_FINAL = setdiff(LIST_OF_PROBLEMS_FINAL, [f])
+            try
+                grid_size = metadata(f)[:grid_size]
+
+                # do we keep or remove the problem from the list
+                keep_problem = true
+
+                #
+                DEBUG && println("\n", "┌─ ", string(f), " (OptimalControl)")
+                DEBUG && println("│")
+
+                # Set up the model
+                docp = OptimalControlProblems.eval(f)(
+                    OptimalControlBackend(); grid_size=grid_size
+                )
+                nlp = nlp_model(docp)
+
+                # Solve the model
+                print("  First solve:  ");
+                @time sol = NLPModelsIpopt.ipopt(nlp; options_ipopt...)
+                print("  Second solve: ");
+                @time sol = NLPModelsIpopt.ipopt(nlp; options_ipopt...)
+
+                # Infos
+                DEBUG && println("│")
+                DEBUG && print(
+                    "│ sol.status: ",
+                    sol.status,
+                    ", objective: ",
+                    sol.objective,
+                    ", iterations: ",
+                    sol.iter,
+                )
+
+                # Test
+                res = @my_test_broken (sol.status == :first_order || sol.status == :acceptable)
+                keep_problem = keep_problem && res
+                DEBUG && res && println(", \033[1;32mPass\033[0m")
+                DEBUG && !res && println(", \033[1;31mFail\033[0m")
+                DEBUG && println("│")
+                DEBUG && println("└─")
+
+                # do we keep or remove the problem from the list
+                if !keep_problem
+                    global LIST_OF_PROBLEMS_FINAL
+                    LIST_OF_PROBLEMS_FINAL = setdiff(LIST_OF_PROBLEMS_FINAL, [f])
+                end
+            catch e
+                handle_solver_error(e, f)
             end
         end
     end

test/test_OptimalControl_s.jl

@@ -12,50 +12,54 @@ function test_OptimalControl_s()
 
     for f in LIST_OF_PROBLEMS
         @testset "$(f)" verbose=VERBOSE begin
-            grid_size = metadata(f)[:grid_size]
-
-            # do we keep or remove the problem from the list
-            keep_problem = true
-
-            #
-            DEBUG && println("\n", "┌─ ", string(f), " (OptimalControl_s)")
-            DEBUG && println("│")
-
-            # Set up the model
-            docp = OptimalControlProblems.eval(Symbol(f, :_s))(
-                OptimalControlBackend(), :madnlp, :exa; grid_size=grid_size
-            )
-            nlp = nlp_model(docp)
-
-            # Solve the model
-            print("  First solve:  ");
-            @time sol = madnlp(nlp; options_madnlp...)
-            print("  Second solve: ");
-            @time sol = madnlp(nlp; options_madnlp...)
-
-            # Infos
-            DEBUG && println("│")
-            DEBUG && print(
-                "│ sol.status: ",
-                sol.status,
-                ", objective: ",
-                sol.objective,
-                ", iterations: ",
-                sol.iter,
-            )
-
-            # Test
-            res = @my_test_broken (sol.status == MadNLP.SOLVE_SUCCEEDED) #:first_order || sol.status == :acceptable)
-            keep_problem = keep_problem && res
-            DEBUG && res && println(", \033[1;32mPass\033[0m")
-            DEBUG && !res && println(", \033[1;31mFail\033[0m")
-            DEBUG && println("│")
-            DEBUG && println("└─")
-
-            # do we keep or remove the problem from the list
-            if !keep_problem
-                global LIST_OF_PROBLEMS_FINAL
-                LIST_OF_PROBLEMS_FINAL = setdiff(LIST_OF_PROBLEMS_FINAL, [f])
+            try
+                grid_size = metadata(f)[:grid_size]
+
+                # do we keep or remove the problem from the list
+                keep_problem = true
+
+                #
+                DEBUG && println("\n", "┌─ ", string(f), " (OptimalControl_s)")
+                DEBUG && println("│")
+
+                # Set up the model
+                docp = OptimalControlProblems.eval(Symbol(f, :_s))(
+                    OptimalControlBackend(), :madnlp, :exa; grid_size=grid_size
+                )
+                nlp = nlp_model(docp)
+
+                # Solve the model
+                print("  First solve:  ");
+                @time sol = madnlp(nlp; options_madnlp...)
+                print("  Second solve: ");
+                @time sol = madnlp(nlp; options_madnlp...)
+
+                # Infos
+                DEBUG && println("│")
+                DEBUG && print(
+                    "│ sol.status: ",
+                    sol.status,
+                    ", objective: ",
+                    sol.objective,
+                    ", iterations: ",
+                    sol.iter,
+                )
+
+                # Test
+                res = @my_test_broken (sol.status == MadNLP.SOLVE_SUCCEEDED) #:first_order || sol.status == :acceptable)
+                keep_problem = keep_problem && res
+                DEBUG && res && println(", \033[1;32mPass\033[0m")
+                DEBUG && !res && println(", \033[1;31mFail\033[0m")
+                DEBUG && println("│")
+                DEBUG && println("└─")
+
+                # do we keep or remove the problem from the list
+                if !keep_problem
+                    global LIST_OF_PROBLEMS_FINAL
+                    LIST_OF_PROBLEMS_FINAL = setdiff(LIST_OF_PROBLEMS_FINAL, [f])
+                end
+            catch e
+                handle_solver_error(e, f)
             end
         end
     end