140 lines
4.8 KiB
Python
140 lines
4.8 KiB
Python
# Copyright 2010-2018 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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"""Linear programming examples that show how to use the APIs."""
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from __future__ import print_function
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from ortools.linear_solver import pywraplp
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def Announce(solver, api_type):
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print('---- Linear programming example with ' + solver + ' (' + api_type +
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') -----')
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def RunLinearExampleNaturalLanguageAPI(optimization_problem_type):
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"""Example of simple linear program with natural language API."""
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solver = pywraplp.Solver.CreateSolver(optimization_problem_type)
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if not solver:
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return
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Announce(optimization_problem_type, 'natural language API')
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infinity = solver.infinity()
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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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solver.Maximize(10 * x1 + 6 * x2 + 4 * x3)
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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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c2 = solver.Add(sum_of_vars <= 100.0, 'OtherConstraintName')
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SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2])
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# Print a linear expression's solution value.
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print('Sum of vars: %s = %s' % (sum_of_vars, sum_of_vars.solution_value()))
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def RunLinearExampleCppStyleAPI(optimization_problem_type):
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"""Example of simple linear program with the C++ style API."""
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solver = pywraplp.Solver.CreateSolver(optimization_problem_type)
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if not solver:
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return
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Announce(optimization_problem_type, 'C++ style API')
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infinity = solver.infinity()
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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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# Maximize 10 * x1 + 6 * x2 + 4 * x3.
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objective = solver.Objective()
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objective.SetCoefficient(x1, 10)
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objective.SetCoefficient(x2, 6)
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objective.SetCoefficient(x3, 4)
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objective.SetMaximization()
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# x1 + x2 + x3 <= 100.
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c0 = solver.Constraint(-infinity, 100.0, 'c0')
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c0.SetCoefficient(x1, 1)
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c0.SetCoefficient(x2, 1)
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c0.SetCoefficient(x3, 1)
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# 10 * x1 + 4 * x2 + 5 * x3 <= 600.
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c1 = solver.Constraint(-infinity, 600.0, 'c1')
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c1.SetCoefficient(x1, 10)
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c1.SetCoefficient(x2, 4)
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c1.SetCoefficient(x3, 5)
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# 2 * x1 + 2 * x2 + 6 * x3 <= 300.
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c2 = solver.Constraint(-infinity, 300.0, 'c2')
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c2.SetCoefficient(x1, 2)
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c2.SetCoefficient(x2, 2)
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c2.SetCoefficient(x3, 6)
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SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2])
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def SolveAndPrint(solver, variable_list, constraint_list):
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"""Solve the problem and print the solution."""
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print('Number of variables = %d' % solver.NumVariables())
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print('Number of constraints = %d' % solver.NumConstraints())
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result_status = solver.Solve()
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# The problem has an optimal solution.
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assert result_status == pywraplp.Solver.OPTIMAL
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# The solution looks legit (when using solvers others than
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# GLOP_LINEAR_PROGRAMMING, verifying the solution is highly recommended!).
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assert solver.VerifySolution(1e-7, True)
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print('Problem solved in %f milliseconds' % solver.wall_time())
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# The objective value of the solution.
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print('Optimal objective value = %f' % solver.Objective().Value())
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# The value of each variable in the solution.
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for variable in variable_list:
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print('%s = %f' % (variable.name(), variable.solution_value()))
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print('Advanced usage:')
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print('Problem solved in %d iterations' % solver.iterations())
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for variable in variable_list:
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print('%s: reduced cost = %f' %
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(variable.name(), variable.reduced_cost()))
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activities = solver.ComputeConstraintActivities()
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for i, constraint in enumerate(constraint_list):
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print(('constraint %d: dual value = %f\n'
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' activity = %f' %
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(i, constraint.dual_value(), activities[constraint.index()])))
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def main():
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RunLinearExampleNaturalLanguageAPI('GLOP')
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RunLinearExampleNaturalLanguageAPI('GLPK_LP')
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RunLinearExampleNaturalLanguageAPI('CLP')
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RunLinearExampleCppStyleAPI('GLOP')
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RunLinearExampleCppStyleAPI('GLPK_LP')
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RunLinearExampleCppStyleAPI('CLP')
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if __name__ == '__main__':
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main()
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