99 lines
3.3 KiB
Python
Executable File
99 lines
3.3 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2010-2022 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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"""Simple unit tests for python/linear_solver.i. Not exhaustive."""
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import unittest
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from ortools.linear_solver import linear_solver_pb2
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from ortools.linear_solver import pywraplp
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from google.protobuf import text_format
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TEXT_MODEL = """
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variable {
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lower_bound: 1.0
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upper_bound: 10.0
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objective_coefficient: 2.0
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}
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variable {
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lower_bound: 1.0
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upper_bound: 10.0
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objective_coefficient: 1.0
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}
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constraint {
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lower_bound: -10000.0
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upper_bound: 4.0
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var_index: 0
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var_index: 1
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coefficient: 1.0
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coefficient: 2.0
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}
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"""
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class PyWrapLp(unittest.TestCase):
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def test_proto(self):
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input_proto = linear_solver_pb2.MPModelProto()
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text_format.Merge(TEXT_MODEL, input_proto)
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solver = pywraplp.Solver.CreateSolver('CBC')
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if not solver:
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return
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# For now, create the model from the proto by parsing the proto
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errors = solver.LoadModelFromProto(input_proto)
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self.assertFalse(errors)
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solver.Solve()
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# Fill solution
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solution = linear_solver_pb2.MPSolutionResponse()
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solver.FillSolutionResponseProto(solution)
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self.assertEqual(solution.objective_value, 3.0)
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self.assertEqual(solution.variable_value[0], 1.0)
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self.assertEqual(solution.variable_value[1], 1.0)
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self.assertEqual(solution.best_objective_bound, 3.0)
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def test_external_api(self):
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solver = pywraplp.Solver.CreateSolver('GLOP')
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infinity = solver.Infinity()
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infinity2 = solver.infinity()
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self.assertEqual(infinity, infinity2)
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# x1, x2 and x3 are continuous non-negative variables.
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x1 = solver.NumVar(0.0, infinity, 'x1')
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x2 = solver.NumVar(0.0, infinity, 'x2')
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x3 = solver.NumVar(0.0, infinity, 'x3')
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self.assertEqual(x1.Lb(), 0)
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self.assertEqual(x1.Ub(), infinity)
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self.assertFalse(x1.Integer())
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solver.Maximize(10 * x1 + 6 * x2 + 4 * x3 + 5)
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self.assertEqual(solver.Objective().Offset(), 5)
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c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, 'ConstraintName0')
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c1 = solver.Add(2 * x1 + 2 * x2 + 6 * x3 <= 300)
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sum_of_vars = sum([x1, x2, x3])
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solver.Add(sum_of_vars <= 100.0, 'OtherConstraintName')
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self.assertEqual(c1.Lb(), -infinity)
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self.assertEqual(c1.Ub(), 300)
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c1.SetLb(-100000)
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self.assertEqual(c1.Lb(), -100000)
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c1.SetUb(301)
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self.assertEqual(c1.Ub(), 301)
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solver.SetTimeLimit(10000)
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result_status = solver.Solve()
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# The problem has an optimal solution.
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self.assertEqual(result_status, pywraplp.Solver.OPTIMAL)
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self.assertAlmostEqual(x1.ReducedCost(), 0.0)
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self.assertAlmostEqual(c0.DualValue(), 0.6666666666666667)
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if __name__ == '__main__':
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unittest.main()
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