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_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 Outer Approximation')
opt.solve(model, strategy='OA',
mip_solver=required_solvers[1],
nlp_solver=required_solvers[0],
)
from pyomo.contrib.gdpopt.util import create_utility_block, time_code, process_objective, setup_results_object
from pyomo.contrib.mindtpy.initialization import MindtPy_initialize_main, init_rNLP
from pyomo.contrib.mindtpy.feasibility_pump import generate_norm_constraint, handle_fp_main_tc
from pyomo.core import Block, ConstraintList
from pyomo.contrib.mindtpy.mip_solve import solve_main, handle_main_other_conditions
from pyomo.opt import SolutionStatus, SolverStatus
from pyomo.core import (Constraint, Objective, TransformationFactory, minimize,
Var, RangeSet, NonNegativeReals)
from pyomo.contrib.mindtpy.iterate import algorithm_should_terminate
nonconvex_model_list = [EightProcessFlowsheet(convex=False)]
LP_model = LP_unbounded()
LP_model._generate_model()
QCP_model = QCP_simple()
QCP_model._generate_model()
extreme_model_list = [LP_model.model, QCP_model.model]
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, ))
class TestMindtPy(unittest.TestCase):