a7f49a2585
* rename swig files .i in .swig * update constraint_solver and routing * backport math_opt changes * move dynamic loading to ortools/third_party_solvers
83 lines
2.4 KiB
Python
83 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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"""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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if not solver:
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return
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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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print(f"Solving with {solver.SolverVersion()}")
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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(f"Objective value = {solver.Objective().Value():0.1f}")
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print(f"x = {x.solution_value():0.1f}")
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print(f"y = {y.solution_value():0.1f}")
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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(f"Problem solved in {solver.wall_time():d} milliseconds")
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print(f"Problem solved in {solver.iterations():d} iterations")
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# [END advanced]
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LinearProgrammingExample()
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# [END program]
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