def test_is_feasible_function(self):
m = ConcreteModel()
m.x = Var(bounds=(0, 3), initialize=2)
m.c = Constraint(expr=m.x == 2)
self.assertTrue(is_feasible(m, GDPoptSolver.CONFIG()))
m.c2 = Constraint(expr=m.x <= 1)
self.assertFalse(is_feasible(m, GDPoptSolver.CONFIG()))
m = ConcreteModel()
m.x = Var(bounds=(0, 3), initialize=2)
m.c = Constraint(expr=m.x >= 5)
self.assertFalse(is_feasible(m, GDPoptSolver.CONFIG()))
m = ConcreteModel()
m.x = Var(bounds=(3, 3), initialize=2)
self.assertFalse(is_feasible(m, GDPoptSolver.CONFIG()))
m = ConcreteModel()
m.x = Var(bounds=(0, 1), initialize=2)
self.assertFalse(is_feasible(m, GDPoptSolver.CONFIG()))
m = ConcreteModel()
m.x = Var(bounds=(0, 1), initialize=2)
m.d = Disjunct()
with self.assertRaisesRegexp(NotImplementedError,
"Found active disjunct"):
is_feasible(m, GDPoptSolver.CONFIG())
def test_solve_linear_GDP_unbounded(self):
m = ConcreteModel()
m.GDPopt_utils = Block()
m.x = Var(bounds=(-1, 10))
m.y = Var(bounds=(2, 3))
m.z = Var()
m.d = Disjunction(expr=[[m.x + m.y >= 5], [m.x - m.y <= 3]])
m.o = Objective(expr=m.z)
m.GDPopt_utils.variable_list = [m.x, m.y, m.z]
m.GDPopt_utils.disjunct_list = [
m.d._autodisjuncts[0], m.d._autodisjuncts[1]
]
output = StringIO()
with LoggingIntercept(output, 'pyomo.contrib.gdpopt', logging.WARNING):
solve_linear_GDP(m, GDPoptSolveData(),
GDPoptSolver.CONFIG(dict(mip_solver=mip_solver)))
self.assertIn(
"Linear GDP was unbounded. Resolving with arbitrary bound values",
output.getvalue().strip())
