def test_time_limit(self):
with SolverFactory('mindtpy') as opt:
model = ConstraintQualificationExample()
opt.solve(model, strategy='OA',
time_limit=1,
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0]
)
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='OA',
iteration_limit=1,
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0])
def test_OA_ConstraintQualificationExample(self):
with SolverFactory('mindtpy') as opt:
model = ConstraintQualificationExample()
print('\n Solving Constraint Qualification Example with Outer Approximation')
results = opt.solve(model, strategy='OA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0]
)
self.assertIs(results.solver.termination_condition,
TerminationCondition.optimal)
self.assertAlmostEqual(value(model.objective.expr), 3, places=2)
def test_lazy_OA_ConstraintQualificationExample(self):
with SolverFactory('mindtpy') as opt:
model = ConstraintQualificationExample()
print('\n Solving ConstraintQualificationExample with LP/NLP')
results = opt.solve(model,
strategy='OA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
single_tree=True)
self.assertIs(results.solver.termination_condition,
TerminationCondition.feasible)
self.assertAlmostEqual(value(model.objective.expr), 3, 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_fp_ConstraintQualificationExample(self):
"""Test the feasibility pump algorithm."""
# TODO: bug fix
with SolverFactory('mindtpy') as opt:
model = ConstraintQualificationExample()
print('\n Solving ProposalModel using feasibility pump')
results = opt.solve(model,
strategy='FP',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
iteration_limit=30)
self.assertTrue(is_feasible(model, self.get_config(opt)))
def test_GOA_ConstraintQualificationExample_integer_cut_tabu_list(self):
with SolverFactory('mindtpy') as opt:
model = ConstraintQualificationExample()
print(
'\n Solving Constraint Qualification Example with global Outer Approximation(no-good cuts, tabu list)'
)
results = opt.solve(model,
strategy='GOA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
use_tabu_list=True)
self.assertIn(
results.solver.termination_condition,
[TerminationCondition.optimal, TerminationCondition.feasible])
self.assertAlmostEqual(value(model.objective.expr), 3, places=2)
required_nlp_solvers = 'ipopt'
required_mip_solvers = ['cplex_persistent', 'gurobi_persistent']
available_mip_solvers = [
s for s in required_mip_solvers if SolverFactory(s).available(False)
]
if SolverFactory(required_nlp_solvers).available(
False) and available_mip_solvers:
subsolvers_available = True
else:
subsolvers_available = False
model_list = [
EightProcessFlowsheet(convex=True),
ConstraintQualificationExample(),
SimpleMINLP()
]
@unittest.skipIf(not subsolvers_available,
'Required subsolvers %s are not available' %
([required_nlp_solvers] + required_mip_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."""
@unittest.skipUnless('cplex_persistent' in available_mip_solvers,
'cplex_persistent solver is not available')
def test_LPNLP_CPLEX(self):
"""Test the LP/NLP decomposition algorithm."""
