8bb54b04ef
FYI: find ortools \( -type d -name .git -prune \) -o -type f -print0 | xargs -0 sed -i 's/\(Copyright 2010\)-2018/\1-2021/g'
77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
# Copyright 2010-2021 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 optimization example."""
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# [START program]
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# [START import]
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from ortools.linear_solver import pywraplp
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# [END import]
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def LinearProgrammingExample():
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"""Linear programming sample."""
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# Instantiate a Glop solver, naming it LinearExample.
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# [START solver]
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solver = pywraplp.Solver.CreateSolver('GLOP')
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# [END solver]
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# Create the two variables and let them take on any non-negative value.
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# [START variables]
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x = solver.NumVar(0, solver.infinity(), 'x')
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y = solver.NumVar(0, solver.infinity(), 'y')
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print('Number of variables =', solver.NumVariables())
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# [END variables]
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# [START constraints]
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# Constraint 0: x + 2y <= 14.
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solver.Add(x + 2 * y <= 14.0)
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# Constraint 1: 3x - y >= 0.
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solver.Add(3 * x - y >= 0.0)
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# Constraint 2: x - y <= 2.
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solver.Add(x - y <= 2.0)
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print('Number of constraints =', solver.NumConstraints())
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# [END constraints]
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# [START objective]
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# Objective function: 3x + 4y.
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solver.Maximize(3 * x + 4 * y)
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# [END objective]
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# Solve the system.
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# [START solve]
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status = solver.Solve()
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# [END solve]
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# [START print_solution]
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if status == pywraplp.Solver.OPTIMAL:
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print('Solution:')
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print('Objective value =', solver.Objective().Value())
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print('x =', x.solution_value())
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print('y =', y.solution_value())
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else:
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print('The problem does not have an optimal solution.')
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# [END print_solution]
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# [START advanced]
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print('\nAdvanced usage:')
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print('Problem solved in %f milliseconds' % solver.wall_time())
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print('Problem solved in %d iterations' % solver.iterations())
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# [END advanced]
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LinearProgrammingExample()
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# [END program]
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