def test_fp_MINLP3_simple(self):
"""Test the feasibility pump algorithm."""
with SolverFactory('mindtpy') as opt:
model = SimpleMINLP3()
print('\n Solving SimpleMINLP3 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_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_FP_OA_MINLP3_simple(self):
"""Test the FP-OA algorithm."""
with SolverFactory('mindtpy') as opt:
model = SimpleMINLP3()
print('\n Solving SimpleMINLP3 using FP-OA')
results = opt.solve(model,
strategy='OA',
init_strategy='FP',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
iteration_limit=30)
self.assertIs(results.solver.termination_condition,
TerminationCondition.optimal)
self.assertAlmostEqual(value(model.cost.expr), -5.512, places=2)
def test_OA_MINLP3_simple(self):
"""Test the outer approximation decomposition algorithm."""
with SolverFactory('mindtpy') as opt:
model = SimpleMINLP3()
print('\n Solving problem with Outer Approximation')
results = opt.solve(model,
strategy='OA',
init_strategy='initial_binary',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
obj_bound=10)
self.assertIs(results.solver.termination_condition,
TerminationCondition.optimal)
self.assertAlmostEqual(value(model.cost.expr), -5.512, places=2)
def test_lazy_OA_MINLP3_simple(self):
"""Test the LP/NLP decomposition algorithm."""
with SolverFactory('mindtpy') as opt:
model = SimpleMINLP3()
print('\n Solving MINLP3_simple problem with LP/NLP')
results = opt.solve(model,
strategy='OA',
init_strategy='initial_binary',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
obj_bound=10,
single_tree=True)
# TODO: fix the bug of bound here
# self.assertIs(results.solver.termination_condition,
# TerminationCondition.optimal)
self.assertAlmostEqual(value(model.cost.expr), -5.512, places=2)
def test_GOA_MINLP3_simple(self):
"""Test the global outer approximation decomposition algorithm."""
with SolverFactory('mindtpy') as opt:
model = SimpleMINLP3()
print('\n Solving MINLP3_simple problem with Outer Approximation')
results = opt.solve(model,
strategy='GOA',
init_strategy='initial_binary',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
obj_bound=10,
add_nogood_cuts=True,
use_mcpp=True,
single_tree=True)
self.assertIs(results.solver.termination_condition,
TerminationCondition.feasible)
self.assertAlmostEqual(value(model.cost.expr), -5.512, places=2)
from pyomo.contrib.mindtpy.tests.constraint_qualification_example import ConstraintQualificationExample
from pyomo.environ import SolverFactory, value
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
model_list = [EightProcessFlowsheet(convex=True),
ConstraintQualificationExample(),
SimpleMINLP(),
SimpleMINLP2(),
SimpleMINLP3(),
ProposalModel(),
]
@unittest.skipIf(not subsolvers_available,
'Required subsolvers %s are not available'
% (required_solvers,))
class TestMindtPy(unittest.TestCase):
"""Tests for the MindtPy solver plugin."""
def test_ECP(self):
"""Test the extended cutting plane decomposition algorithm."""
with SolverFactory('mindtpy') as opt:
for model in model_list:
results = opt.solve(model, strategy='ECP',