def test_time_limit(self):
with SolverFactory('mindtpy') as opt:
model = OnlineDocExample()
print('\n Solving problem with Outer Approximation')
opt.solve(model,
strategy='OA',
time_limit=1,
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0])
def test_OA_OnlineDocExample(self):
with SolverFactory('mindtpy') as opt:
model = OnlineDocExample()
print('\n Solving problem with Outer Approximation')
opt.solve(model,
strategy='OA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0])
self.assertAlmostEqual(value(model.objective.expr), 3, places=2)
def test_fp_OnlineDocExample(self):
"""Test the feasibility pump algorithm."""
"""TODO: bug fix"""
with SolverFactory('mindtpy') as opt:
model = OnlineDocExample()
print('\n Solving OnlineDocExample using feasibility pump')
results = opt.solve(model,
strategy='FP',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
iteration_limit=0)
self.assertTrue(is_feasible(model, self.get_config(opt)))
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_OA_OnlineDocExample(self):
with SolverFactory('mindtpy') as opt:
model = OnlineDocExample()
print('\n Solving Online Doc 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), 2.438447, places=2)
def test_GOA_OnlineDocExample_L_infinity_norm(self):
with SolverFactory('mindtpy') as opt:
model = OnlineDocExample()
print(
'\n Solving Online Doc Example with global Outer Approximation'
)
results = opt.solve(model,
strategy='GOA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
feasibility_norm="L_infinity",
add_nogood_cuts=True,
single_tree=True)
self.assertIs(results.solver.termination_condition,
TerminationCondition.optimal)
self.assertAlmostEqual(value(model.objective.expr),
2.438447,
places=2)
from pyomo.contrib.mindtpy.tests.online_doc_example import OnlineDocExample
from pyomo.environ import SolverFactory, value, maximize
from pyomo.solvers.tests.models.LP_unbounded import LP_unbounded
from pyomo.solvers.tests.models.QCP_simple import QCP_simple
from pyomo.opt import TerminationCondition
full_model_list = [EightProcessFlowsheet(convex=True),
ConstraintQualificationExample(),
SimpleMINLP(),
SimpleMINLP2(),
SimpleMINLP3(),
SimpleMINLP4(),
SimpleMINLP5(),
ProposalModel(),
OnlineDocExample()
]
model_list = [EightProcessFlowsheet(convex=True),
ConstraintQualificationExample(),
SimpleMINLP2(),
# SimpleMINLP(),
# SimpleMINLP3(),
# SimpleMINLP4(),
# SimpleMINLP5(),
# ProposalModel(),
# OnlineDocExample()
]
nonconvex_model_list = [EightProcessFlowsheet(convex=False)]
LP_model = LP_unbounded()
LP_model._generate_model()
