def test_equality_constraints_on_disjuncts_with_fme(self):
m = models.oneVarDisj_2pts()
m.obj.expr = m.x + m.disj1.indicator_var
m.obj.sense = maximize
TransformationFactory('gdp.cuttingplane').apply_to(
m,
create_cuts=create_cuts_fme,
post_process_cut=None, verbose=True, solver='gurobi',
# don't actually need this, but taking the excuse to set solver
# options
solver_options={'FeasibilityTol': 1e-8},
cuts_name="cuts", bigM=5)
# rBigM first iteration solve will give (x = 3, Y = 0.6). If we don't
# catch equality constraints, we don't get a cut. But we need to get
# x + Y <= 1. (Where Y is the indicator that x = 0).
self.assertEqual(len(m.cuts), 1)
cut = m.cuts[0]
self.assertEqual(cut.lower, 0)
self.assertIsNone(cut.upper)
repn = generate_standard_repn(cut.body)
self.assertTrue(repn.is_linear())
self.assertEqual(len(repn.linear_vars), 2)
self.assertIs(repn.linear_vars[0], m.disj1.indicator_var)
self.assertEqual(repn.linear_coefs[0], 1)
self.assertIs(repn.linear_vars[1], m.x)
self.assertEqual(repn.linear_coefs[1], -1)
def test_no_cuts_for_optimal_m_inf_norm(self):
m = models.oneVarDisj_2pts()
TransformationFactory('gdp.cuttingplane').apply_to(
m,
norm=float('inf'),
post_process_cut=None
)
self.check_no_cuts_for_optimal_m(m)
def test_no_cuts_for_optimal_m_fme(self):
m = models.oneVarDisj_2pts()
TransformationFactory('gdp.cuttingplane').apply_to(
m,
create_cuts=create_cuts_fme,
post_process_cut=None
)
self.check_no_cuts_for_optimal_m(m)
def test_no_cuts_for_optimal_m(self):
m = models.oneVarDisj_2pts()
TransformationFactory('gdp.cuttingplane').apply_to(m)
self.check_no_cuts_for_optimal_m(m)
