def test_GOA_Nonconvex3(self):
with SolverFactory('mindtpy') as opt:
model = Nonconvex3()
print('\n Solving Nonconvex3 with global Outer Approximation')
results = opt.solve(model,
strategy='GOA',
mip_solver=required_solvers[1],
nlp_solver='baron',
single_tree=True)
self.assertIs(results.solver.termination_condition,
TerminationCondition.optimal)
self.assertAlmostEqual(value(model.objective.expr), 31, places=2)
from pyomo.environ import *
from pyomo.opt import TerminationCondition
required_solvers = ('baron', 'cplex_persistent')
if not all(SolverFactory(s).available(False) for s in required_solvers):
subsolvers_available = False
elif not SolverFactory('baron').license_is_valid():
subsolvers_available = False
else:
subsolvers_available = True
model_list = [
EightProcessFlowsheet(convex=False),
Nonconvex1(),
Nonconvex2(),
Nonconvex3(),
Nonconvex4(),
]
@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')
@unittest.skipIf(not pyomo.contrib.mcpp.pyomo_mcpp.mcpp_available(),
'MC++ is not available')
class TestMindtPy(unittest.TestCase):
"""Tests for the MindtPy solver plugin."""
def test_GOA(self):
"""Test the global outer approximation decomposition algorithm."""