Allow top-down configuration of target statuses (#20)

* allow to override target statuses with Dicts, globally
 - before, one could only override the termination_target and primal_target
   individually
 - now, each test case specifies whether the problem is FEASIBLE or INFEASIBLE,
   when calling check_status
 - a pair of default Dicts, mapping problem type to target status is now passed
   down from the helper functions to the test functions

src/MINLPTests.jl

@@ -15,13 +15,42 @@ const PRIMAL_TOL = 1e-6
 # Absolue tolerance when checking the dual solution value.
 const DUAL_TOL   = 1e-6
 
+###
+### Default expected status codes for different types of problems and solvers.
+###
+
+# We only distinguish between feasible and infeasible problems now.
+@enum ProblemTypeCode FEASIBLE_PROBLEM INFEASIBLE_PROBLEM
+
+# Target status codes for local solvers:
+const TERMINATION_TARGET_LOCAL = Dict(
+    FEASIBLE_PROBLEM => JuMP.MOI.LOCALLY_SOLVED,
+    INFEASIBLE_PROBLEM => JuMP.MOI.LOCALLY_INFEASIBLE,
+)
+const PRIMAL_TARGET_LOCAL = Dict(
+    FEASIBLE_PROBLEM => JuMP.MOI.FEASIBLE_POINT,
+    INFEASIBLE_PROBLEM => JuMP.MOI.INFEASIBLE_POINT,
+)
+
+# Target status codes for global solvers:
+const TERMINATION_TARGET_GLOBAL = Dict(
+    FEASIBLE_PROBLEM => JuMP.MOI.OPTIMAL,
+    INFEASIBLE_PROBLEM => JuMP.MOI.INFEASIBLE,
+)
+const PRIMAL_TARGET_GLOBAL = Dict(
+    FEASIBLE_PROBLEM => JuMP.MOI.FEASIBLE_POINT,
+    INFEASIBLE_PROBLEM => JuMP.MOI.NO_SOLUTION,
+)
+
 ###
 ### Helper functions for the tests.
 ###
 
-function check_status(model, termination_target, primal_target)
-    @test JuMP.termination_status(model) == termination_target
-    @test JuMP.primal_status(model) == primal_target
+function check_status(model, problem_type::ProblemTypeCode,
+                      termination_target=TERMINATION_TARGET_LOCAL,
+                      primal_target=PRIMAL_TARGET_LOCAL)
+    @test JuMP.termination_status(model) == termination_target[problem_type]
+    @test JuMP.primal_status(model) == primal_target[problem_type]
 end
 
 function check_objective(model, solution; tol = OPT_TOL)
@@ -71,12 +100,16 @@ end
 """
 function test_directory(
         directory, optimizer; exclude=String[], include=String[],
-        objective_tol = OPT_TOL, primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL)
+        objective_tol = OPT_TOL, primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
+
     @testset "$(directory)" begin
         @testset "$(model_name)" for model_name in list_of_models(directory, exclude, include)
             function_name = string(replace(directory, "-" => "_"), "_", model_name)
             model_function = getfield(MINLPTests, Symbol(function_name))
-            model_function(optimizer, objective_tol, primal_tol, dual_tol)
+            model_function(optimizer, objective_tol, primal_tol, dual_tol,
+                           termination_target, primal_target)
         end
     end
 end
@@ -102,26 +135,38 @@ end
 
 function test_nlp(
         optimizer; exclude = String[], objective_tol = OPT_TOL,
-        primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL)
+        primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     test_directory("nlp", optimizer;
         exclude = exclude, objective_tol = objective_tol,
-        primal_tol = primal_tol, dual_tol = dual_tol)
+        primal_tol = primal_tol, dual_tol = dual_tol,
+        termination_target = termination_target,
+        primal_target = primal_target)
 end
 
 function test_nlp_cvx(
         optimizer; exclude = String[], objective_tol = OPT_TOL,
-        primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL)
+        primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     test_directory("nlp-cvx", optimizer;
         exclude = exclude, objective_tol = objective_tol,
-        primal_tol = primal_tol, dual_tol = dual_tol)
+        primal_tol = primal_tol, dual_tol = dual_tol,
+        termination_target = termination_target,
+        primal_target = primal_target)
 end
 
 function test_nlp_mi(
         optimizer; exclude = String[], objective_tol = OPT_TOL,
-        primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL)
+        primal_tol = PRIMAL_TOL, dual_tol = DUAL_TOL,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     test_directory("nlp-mi", optimizer;
         exclude = exclude, objective_tol = objective_tol,
-        primal_tol = primal_tol, dual_tol = dual_tol)
+        primal_tol = primal_tol, dual_tol = dual_tol,
+        termination_target = termination_target,
+        primal_target = primal_target)
 end
 
 ### Tests that haven't been updated.

src/nlp-cvx/001_010.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_001_010(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_001_010(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     # Test Goals:
     # - linear objective
     # - quadratic objective
@@ -23,7 +23,7 @@ function nlp_cvx_001_010(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, -2.0430107680954848, tol = objective_tol)
     check_solution([x,y], [-2.0430107680954848, -0.4301075068564087], tol = primal_tol)
 

src/nlp-cvx/001_011.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_001_011(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_001_011(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -15,7 +15,7 @@ function nlp_cvx_001_011(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 0.0, tol = objective_tol)
     check_solution([x,y], [1.0, 2.0], tol = primal_tol)
 

src/nlp-cvx/002_010.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_002_010(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_002_010(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     # Test Goals:
     # - linear objective
     # - quadratic objective
@@ -24,7 +24,7 @@ function nlp_cvx_002_010(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 3.9655172067026196, tol = objective_tol)
     check_solution([x,y], [2.4137930845761546, 1.5517241221264648], tol = primal_tol)
 

src/nlp-cvx/002_011.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_002_011(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_002_011(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -16,7 +16,7 @@ function nlp_cvx_002_011(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 0, tol = objective_tol)
     check_solution([x,y], [3, 2], tol = primal_tol)
 

src/nlp-cvx/101_010.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_101_010(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_101_010(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     # Test Goals:
     # - linear objective
     # - single convex quadratic constraint
@@ -19,7 +19,7 @@ function nlp_cvx_101_010(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, -2/sqrt(2), tol = objective_tol)
     check_solution([x,y], [1/sqrt(2), 1/sqrt(2)], tol = primal_tol)
 

src/nlp-cvx/101_011.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_101_011(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_101_011(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, -2 <= x <= 2)
@@ -11,7 +11,7 @@ function nlp_cvx_101_011(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, -1, tol = objective_tol)
     check_solution([x,y], [1, 0], tol = primal_tol)
 

src/nlp-cvx/101_012.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_101_012(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_101_012(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, -2 <= x <= 2)
@@ -11,7 +11,7 @@ function nlp_cvx_101_012(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 1, tol = objective_tol)
     check_solution([x,y], [1, 0], tol = primal_tol)
 

src/nlp-cvx/102_010.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_102_010(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_102_010(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     # Test Goals:
     # - linear and nonlinear constraints
     # Variants
@@ -21,7 +21,7 @@ function nlp_cvx_102_010(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, -0.974165743715913, tol = objective_tol)
     check_solution([x,y], [0.974165743715913, 0.2258342542139504], tol = primal_tol)
 

src/nlp-cvx/102_011.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_102_011(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_102_011(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -12,7 +12,7 @@ function nlp_cvx_102_011(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 1.2, tol = objective_tol)
     ### Can't test solution point because there are multiple solutions.
 end

src/nlp-cvx/102_012.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_102_012(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_102_012(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -12,7 +12,7 @@ function nlp_cvx_102_012(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 2/sqrt(2), tol = objective_tol)
     check_solution([x,y], [1/sqrt(2), 1/sqrt(2)], tol = primal_tol)
 

src/nlp-cvx/102_013.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_102_013(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_102_013(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -12,7 +12,7 @@ function nlp_cvx_102_013(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 0.72, tol = objective_tol)
     check_solution([x,y], [0.6, 0.6], tol = primal_tol)
 

src/nlp-cvx/102_014.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_102_014(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_102_014(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -12,7 +12,7 @@ function nlp_cvx_102_014(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 0, tol = objective_tol)
     check_solution([x,y], [0.65, 0.65], tol = primal_tol)
 

src/nlp-cvx/103_010.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_103_010(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_103_010(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     # Test Goals:
     # - linear objective
     # - intersection convex quadratic constraints
@@ -22,7 +22,7 @@ function nlp_cvx_103_010(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, 0, tol = objective_tol)
     check_solution([x,y], [0, 0], tol = primal_tol)
 

src/nlp-cvx/103_011.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_103_011(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED,
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_103_011(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -12,7 +12,7 @@ function nlp_cvx_103_011(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, -1, tol = objective_tol)
     check_solution([x,y], [0, 1], tol = primal_tol)
 

src/nlp-cvx/103_012.jl

@@ -1,6 +1,6 @@
-function nlp_cvx_103_012(optimizer, objective_tol, primal_tol, dual_tol;
-        termination_target = JuMP.MOI.LOCALLY_SOLVED, 
-        primal_target = JuMP.MOI.FEASIBLE_POINT)
+function nlp_cvx_103_012(optimizer, objective_tol, primal_tol, dual_tol,
+        termination_target = TERMINATION_TARGET_LOCAL,
+        primal_target = PRIMAL_TARGET_LOCAL)
     m = Model(optimizer)
 
     @variable(m, x)
@@ -12,7 +12,7 @@ function nlp_cvx_103_012(optimizer, objective_tol, primal_tol, dual_tol;
 
     optimize!(m)
 
-    check_status(m, termination_target, primal_target)
+    check_status(m, FEASIBLE_PROBLEM, termination_target, primal_target)
     check_objective(m, -5/4, tol = objective_tol)
     check_solution([x,y], [1/2, 3/4], tol = primal_tol)