58 lines
1.9 KiB
Python
58 lines
1.9 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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"""Testing correctness of the code snippets in the comments of model.py."""
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from typing import Sequence
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from absl import app
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from ortools.math_opt.python import mathopt
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# Model the problem:
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# max 2.0 * x + y
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# s.t. x + y <= 1.5
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# x in {0.0, 1.0}
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# y in [0.0, 2.5]
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#
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def main(argv: Sequence[str]) -> None:
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del argv # Unused.
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model = mathopt.Model(name="my_model")
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x = model.add_binary_variable(name="x")
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y = model.add_variable(lb=0.0, ub=2.5, name="y")
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# We can directly use linear combinations of variables ...
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model.add_linear_constraint(x + y <= 1.5, name="c")
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# ... or build them incrementally.
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objective_expression = 0
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objective_expression += 2 * x
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objective_expression += y
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model.maximize(objective_expression)
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# May raise a RuntimeError on invalid input or internal solver errors.
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result = mathopt.solve(model, mathopt.SolverType.GSCIP)
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if result.termination.reason not in (
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mathopt.TerminationReason.OPTIMAL,
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mathopt.TerminationReason.FEASIBLE,
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):
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raise RuntimeError(f"model failed to solve: {result.termination}")
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print(f"Objective value: {result.objective_value()}")
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print(f"Value for variable x: {result.variable_values()[x]}")
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if __name__ == "__main__":
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app.run(main)
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