#!/usr/bin/env python3 # Copyright 2010-2021 Google LLC # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Integer programming examples that show how to use the APIs.""" # [START program] # [START import] from ortools.linear_solver import pywraplp # [END import] def main(): # [START solver] # Create the mip solver with the SCIP backend. solver = pywraplp.Solver.CreateSolver('SCIP') # [END solver] # [START variables] infinity = solver.infinity() # x and y are integer non-negative variables. x = solver.IntVar(0.0, infinity, 'x') y = solver.IntVar(0.0, infinity, 'y') print('Number of variables =', solver.NumVariables()) # [END variables] # [START constraints] # x + 7 * y <= 17.5. solver.Add(x + 7 * y <= 17.5) # x <= 3.5. solver.Add(x <= 3.5) print('Number of constraints =', solver.NumConstraints()) # [END constraints] # [START objective] # Maximize x + 10 * y. solver.Maximize(x + 10 * y) # [END objective] # [START solve] status = solver.Solve() # [END solve] # [START print_solution] if status == pywraplp.Solver.OPTIMAL: print('Solution:') print('Objective value =', solver.Objective().Value()) print('x =', x.solution_value()) print('y =', y.solution_value()) else: print('The problem does not have an optimal solution.') # [END print_solution] # [START advanced] print('\nAdvanced usage:') print('Problem solved in %f milliseconds' % solver.wall_time()) print('Problem solved in %d iterations' % solver.iterations()) print('Problem solved in %d branch-and-bound nodes' % solver.nodes()) # [END advanced] if __name__ == '__main__': main() # [END program]