5bf70b691f
* CMake has not been updated yet * bazel was compiling at least last week bazel: disable math opt facility_location.py missing some dependencies...
72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
#!/usr/bin/env python3
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# Copyright 2010-2025 Google LLC
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Solves an optimization problem with multiple objectives."""
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from collections.abc import Sequence
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from absl import app
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from absl import flags
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from ortools.math_opt.python import mathopt
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_ENABLE_OUTPUT = flags.DEFINE_bool(
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"enable_output", False, "should the solver logs be turned on"
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)
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def main(argv: Sequence[str]) -> None:
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if len(argv) > 1:
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raise app.UsageError("Too many command-line arguments.")
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model = mathopt.Model()
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# The model is:
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# max x + y + 2 z
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# s.t. x + y + z <= 1.5
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# x, y in [0, 1]
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# z binary
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# With secondary objective
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# max y
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#
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# The first problem is solved by any convex combination of:
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# (0.5, 0, 1) and (0, 0.5, 1)
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# But with the secondary objective, the unique solution is (0, 0.5, 1), with
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# a primary objective value of 2.5 and secondary objective value of 0.5.
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x = model.add_variable(lb=0, ub=1)
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y = model.add_variable(lb=0, ub=1)
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z = model.add_binary_variable()
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model.add_linear_constraint(x + y + z <= 1.5)
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model.maximize(x + y + 2 * z)
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aux = model.add_maximization_objective(y, priority=1)
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result = mathopt.solve(
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model,
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mathopt.SolverType.GUROBI,
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params=mathopt.SolveParameters(enable_output=_ENABLE_OUTPUT.value),
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)
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if result.termination.reason != mathopt.TerminationReason.OPTIMAL:
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raise ValueError(
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f"Expected an optimal termination, found: {result.termination}"
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)
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print(f"primary objective: {result.objective_value()}")
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print(f"x: {result.variable_values(x)}")
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print(f"y: {result.variable_values(y)}")
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print(f"z: {result.variable_values(z)}")
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prim_sol = result.solutions[0].primal_solution
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assert prim_sol is not None
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print(f"secondary objective: {prim_sol.auxiliary_objective_values[aux]}")
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if __name__ == "__main__":
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app.run(main)
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