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40 lines
1.4 KiB
40 lines
1.4 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, 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 miplearn import Instance, BasePyomoSolver, GurobiSolver
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import pyomo.environ as pe
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from tests.solvers import _is_subclass_or_instance
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class PyomoInstanceWithRedundancy(Instance):
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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, 1], domain=pe.Binary)
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model.OBJ = pe.Objective(expr=model.x[0] + model.x[1], sense=pe.maximize)
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model.eq1 = pe.Constraint(expr=model.x[0] + model.x[1] <= 1)
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model.eq2 = pe.Constraint(expr=model.x[0] + model.x[1] <= 2)
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return model
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class GurobiInstanceWithRedundancy(Instance):
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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(2, vtype=GRB.BINARY, name="x")
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model.addConstr(x[0] + x[1] <= 1)
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model.addConstr(x[0] + x[1] <= 2)
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model.setObjective(x[0] + x[1], GRB.MAXIMIZE)
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return model
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def get_instance_with_redundancy(solver):
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if _is_subclass_or_instance(solver, BasePyomoSolver):
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return PyomoInstanceWithRedundancy()
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if _is_subclass_or_instance(solver, GurobiSolver):
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return GurobiInstanceWithRedundancy()
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