# ___________________________________________________________________________ # # Pyomo: Python Optimization Modeling Objects # Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC # Under the terms of Contract DE-NA0003525 with National Technology and # Engineering Solutions of Sandia, LLC, the U.S. Government retains certain # rights in this software. # This software is distributed under the 3-clause BSD License. # ___________________________________________________________________________ import pyomo.kernel as pmo from pyomo.core import ConcreteModel, Param, Var, Objective, Constraint, NonNegativeReals, Binary from pyomo.opt import TerminationCondition from pyomo.solvers.tests.models.base import _BaseTestModel, register_model @register_model class MIQP_simple(_BaseTestModel): """ A mixed-integer model with a quadratic objective and linear constraints """ description = "MIQP_simple" level = ('nightly', 'expensive') capabilities = set(['linear', 'integer', 'quadratic_objective']) def __init__(self): _BaseTestModel.__init__(self) self.add_results(self.description+".json") def _generate_model(self): self.model = ConcreteModel() model = self.model model._name = self.description model.a = Param(initialize=1.0) model.x = Var(within=NonNegativeReals) model.y = Var(within=Binary) model.obj = Objective(expr=model.x**2 + 3.0*model.y**2) model.c1 = Constraint(expr=model.a <= model.y) model.c2 = Constraint(expr=(2.0, model.x/model.a - model.y, 10)) def warmstart_model(self): assert self.model is not None model = self.model model.x.value = 1 model.y.value = 1 def post_solve_test_validation(self, tester, results): if tester is None: assert results['Solver'][0]['termination condition'] in \ (TerminationCondition.optimal, TerminationCondition.locallyOptimal) else: tester.assertIn(results['Solver'][0]['termination condition'], (TerminationCondition.optimal, TerminationCondition.locallyOptimal)) @register_model class MIQP_simple_kernel(MIQP_simple): def _generate_model(self): self.model = pmo.block() model = self.model model._name = self.description model.a = pmo.parameter(value=1.0) model.x = pmo.variable(domain=NonNegativeReals) model.y = pmo.variable(domain=Binary) model.obj = pmo.objective(model.x**2 + 3.0*model.y**2) model.c1 = pmo.constraint(model.a <= model.y) model.c2 = pmo.constraint((2.0, model.x/model.a - model.y, 10))