"""Tests for the MindtPy solver.""" from math import fabs import pyomo.core.base.symbolic import pyutilib.th as unittest from pyomo.contrib.mindtpy.tests.eight_process_problem import \ EightProcessFlowsheet from pyomo.contrib.mindtpy.tests.MINLP_simple import SimpleMINLP as SimpleMINLP from pyomo.contrib.mindtpy.tests.MINLP2_simple import SimpleMINLP as SimpleMINLP2 from pyomo.contrib.mindtpy.tests.MINLP3_simple import SimpleMINLP as SimpleMINLP3 from pyomo.contrib.mindtpy.tests.from_proposal import ProposalModel from pyomo.contrib.mindtpy.tests.constraint_qualification_example import ConstraintQualificationExample from pyomo.contrib.mindtpy.tests.online_doc_example import OnlineDocExample from pyomo.environ import SolverFactory, value from pyomo.environ import * from pyomo.solvers.tests.models.LP_unbounded import LP_unbounded from pyomo.solvers.tests.models.QCP_simple import QCP_simple from pyomo.solvers.tests.models.MIQCP_simple import MIQCP_simple from pyomo.opt import TerminationCondition required_solvers = ('ipopt', 'glpk') # required_solvers = ('gams', 'gams') if all(SolverFactory(s).available() for s in required_solvers): subsolvers_available = True else: subsolvers_available = False @unittest.skipIf(not subsolvers_available, "Required subsolvers %s are not available" % (required_solvers,)) @unittest.skipIf(not pyomo.core.base.symbolic.differentiate_available, "Symbolic differentiation is not available") class TestMindtPy(unittest.TestCase): """Tests for the MindtPy solver plugin.""" def test_ECP_8PP(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = EightProcessFlowsheet() print('\n Solving 8PP problem with extended cutting plane') results = opt.solve(model, strategy='ECP', init_strategy='rNLP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], bound_tolerance=1E-5) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), 68, places=1) def test_ECP_8PP_init_max_binary(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = EightProcessFlowsheet() print('\n Solving 8PP problem with extended cutting plane(max_binary)') results = opt.solve(model, strategy='ECP', init_strategy='max_binary', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], obj_bound=5500) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), 68, places=1) def test_ECP_8PP_L2_norm(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = EightProcessFlowsheet() print('\n Solving 8PP problem with extended cutting plane(max_binary)') results = opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], obj_bound=5500, feasibility_norm='L2') self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), 68, places=1) def test_ECP_8PP_sympy(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = EightProcessFlowsheet() print('\n Solving 8PP problem with extended cutting plane(max_binary)') results = opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], obj_bound=5500, differentiate_mode='sympy') self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), 68, places=1) def test_ECP_MINLP_simple(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = SimpleMINLP() print('\n Solving MINLP_simple problem with extended cutting plane') results = opt.solve(model, strategy='ECP', init_strategy='initial_binary', mip_solver=required_solvers[1], nlp_solver=required_solvers[0]) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), 3.5, places=2) def test_ECP_MINLP2_simple(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = SimpleMINLP2() print('\n Solving MINLP2_simple problem with extended cutting plane') results = opt.solve(model, strategy='ECP', init_strategy='initial_binary', mip_solver=required_solvers[1], nlp_solver=required_solvers[0]) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), 6.00976, places=2) def test_ECP_MINLP3_simple(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = SimpleMINLP3() print('\n Solving MINLP3_simple problem with extended cutting plane') results = opt.solve(model, strategy='ECP', init_strategy='initial_binary', mip_solver=required_solvers[1], nlp_solver=required_solvers[0]) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), -5.512, places=2) def test_ECP_Proposal(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = ProposalModel() print('\n Solving Proposal problem with extended cutting plane') results = opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0]) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.obj.expr), 0.66555, places=2) def test_ECP_ConstraintQualificationExample(self): with SolverFactory('mindtpy') as opt: model = ConstraintQualificationExample() print( '\n Solving Constraint Qualification Example with extended cutting plane') results = opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], bound_tolerance=1e-5 ) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.objective.expr), 3, places=2) def test_ECP_OnlineDocExample(self): with SolverFactory('mindtpy') as opt: model = OnlineDocExample() print('\n Solving Online Doc Example with extended cutting plane') results = opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], bound_tolerance=1e-3 ) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual( value(model.objective.expr), 2.438447, places=2) def test_ECP_OnlineDocExample_L_infinity_norm(self): with SolverFactory('mindtpy') as opt: model = OnlineDocExample() print('\n Solving Online Doc Example with extended cutting plane') results = opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], bound_tolerance=1e-3, feasibility_norm="L_infinity" ) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual( value(model.objective.expr), 2.438447, places=2) # the following tests are used to improve code coverage def test_iteration_limit(self): with SolverFactory('mindtpy') as opt: model = ConstraintQualificationExample() print('\n test iteration_limit to improve code coverage') opt.solve(model, strategy='ECP', iteration_limit=1, mip_solver=required_solvers[1], nlp_solver=required_solvers[0] ) # self.assertAlmostEqual(value(model.objective.expr), 3, places=2) def test_time_limit(self): with SolverFactory('mindtpy') as opt: model = ConstraintQualificationExample() print('\n test time_limit to improve code coverage') opt.solve(model, strategy='ECP', time_limit=1, mip_solver=required_solvers[1], nlp_solver=required_solvers[0] ) def test_LP_case(self): with SolverFactory('mindtpy') as opt: m_class = LP_unbounded() m_class._generate_model() model = m_class.model print('\n Solving LP case with extended cutting plane') opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], ) def test_QCP_case(self): with SolverFactory('mindtpy') as opt: m_class = QCP_simple() m_class._generate_model() model = m_class.model print('\n Solving QCP case with extended cutting plane') opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], ) def test_maximize_obj(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = ProposalModel() model.obj.sense = maximize print('\n test maximize case to improve code coverage') opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], # mip_solver_args={'timelimit': 0.9} ) self.assertAlmostEqual(value(model.obj.expr), 14.83, places=1) def test_rNLP_add_slack(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = EightProcessFlowsheet() print( '\n Test rNLP initialize strategy and add_slack to improve code coverage') opt.solve(model, strategy='ECP', init_strategy='rNLP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], bound_tolerance=1E-5, add_slack=True) self.assertAlmostEqual(value(model.cost.expr), 68, places=1) def test_initial_binary_add_slack(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = SimpleMINLP() print( '\n Test initial_binary initialize strategy and add_slack to improve code coverage') results = opt.solve(model, strategy='ECP', init_strategy='initial_binary', mip_solver=required_solvers[1], nlp_solver=required_solvers[0], add_slack=True) self.assertIs(results.solver.termination_condition, TerminationCondition.optimal) self.assertAlmostEqual(value(model.cost.expr), 3.5, places=2) def test_maximize_obj(self): """Test the extended cutting plane decomposition algorithm.""" with SolverFactory('mindtpy') as opt: model = ProposalModel() model.obj.sense = maximize print('\n test maximize case to improve code coverage') opt.solve(model, strategy='ECP', mip_solver=required_solvers[1], nlp_solver=required_solvers[0] ) self.assertAlmostEqual(value(model.obj.expr), 14.83, places=1) if __name__ == "__main__": unittest.main()