# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Released under the modified BSD license. See COPYING.md for more details. from inspect import isclass from typing import List, Callable, Any from pyomo import environ as pe from miplearn.instance import Instance from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance from miplearn.solvers.gurobi import GurobiSolver from miplearn.solvers.internal import InternalSolver from miplearn.solvers.pyomo.base import BasePyomoSolver from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver from miplearn.solvers.pyomo.xpress import XpressPyomoSolver class InfeasiblePyomoInstance(Instance): def to_model(self) -> pe.ConcreteModel: model = pe.ConcreteModel() model.x = pe.Var([0], domain=pe.Binary) model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize) model.eq = pe.Constraint(expr=model.x[0] >= 2) return model class InfeasibleGurobiInstance(Instance): def to_model(self) -> Any: import gurobipy as gp from gurobipy import GRB model = gp.Model() x = model.addVars(1, vtype=GRB.BINARY, name="x") model.addConstr(x[0] >= 2) model.setObjective(x[0]) return model def _is_subclass_or_instance(obj, parent_class): return isinstance(obj, parent_class) or ( isclass(obj) and issubclass(obj, parent_class) ) def _get_knapsack_instance(solver): if _is_subclass_or_instance(solver, BasePyomoSolver): return KnapsackInstance( weights=[23.0, 26.0, 20.0, 18.0], prices=[505.0, 352.0, 458.0, 220.0], capacity=67.0, ) if _is_subclass_or_instance(solver, GurobiSolver): return GurobiKnapsackInstance( weights=[23.0, 26.0, 20.0, 18.0], prices=[505.0, 352.0, 458.0, 220.0], capacity=67.0, ) assert False def _get_infeasible_instance(solver): if _is_subclass_or_instance(solver, BasePyomoSolver): return InfeasiblePyomoInstance() if _is_subclass_or_instance(solver, GurobiSolver): return InfeasibleGurobiInstance() def _get_internal_solvers() -> List[Callable[[], InternalSolver]]: return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]