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45 lines
1.4 KiB
Python
45 lines
1.4 KiB
Python
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from typing import Any
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from overrides import overrides
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from pyomo import environ as pe
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from miplearn.instance.base import Instance
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.pyomo.base import BasePyomoSolver
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from tests.solvers import _is_subclass_or_instance
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class InfeasiblePyomoInstance(Instance):
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@overrides
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def to_model(self) -> pe.ConcreteModel:
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model = pe.ConcreteModel()
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model.x = pe.Var([0], domain=pe.Binary)
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model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
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model.eq = pe.Constraint(expr=model.x[0] >= 2)
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return model
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class InfeasibleGurobiInstance(Instance):
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@overrides
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def to_model(self) -> Any:
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import gurobipy as gp
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from gurobipy import GRB
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model = gp.Model()
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x = model.addVars(1, vtype=GRB.BINARY, name="x")
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model.addConstr(x[0] >= 2)
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model.setObjective(x[0])
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return model
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def get_infeasible_instance(solver: Any) -> Instance:
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if _is_subclass_or_instance(solver, BasePyomoSolver):
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return InfeasiblePyomoInstance()
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if _is_subclass_or_instance(solver, GurobiSolver):
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return InfeasibleGurobiInstance()
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assert False
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