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ortools-clone/examples/notebook/linear_solver/linear_programming_example.ipynb
Corentin Le Molgat 27121a1068 Update examples/notebook
generated using ./tools/gen_all_notebook.sh
2020-03-04 14:34:33 +01:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Copyright 2010-2018 Google LLC\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"\"\"\"Linear optimization example.\"\"\"\n",
"# [START program]\n",
"from __future__ import print_function\n",
"# [START import]\n",
"from ortools.linear_solver import pywraplp\n",
"\n",
"# [END import]\n",
"\n",
"\n",
"def LinearProgrammingExample():\n",
" \"\"\"Linear programming sample.\"\"\"\n",
" # Instantiate a Glop solver, naming it LinearExample.\n",
" # [START solver]\n",
" solver = pywraplp.Solver('LinearProgrammingExample',\n",
" pywraplp.Solver.GLOP_LINEAR_PROGRAMMING)\n",
" # [END solver]\n",
"\n",
" # Create the two variables and let them take on any non-negative value.\n",
" # [START variables]\n",
" x = solver.NumVar(0, solver.infinity(), 'x')\n",
" y = solver.NumVar(0, solver.infinity(), 'y')\n",
" # [END variables]\n",
"\n",
" # [START constraints]\n",
" # Constraint 0: x + 2y <= 14.\n",
" constraint0 = solver.Constraint(-solver.infinity(), 14)\n",
" constraint0.SetCoefficient(x, 1)\n",
" constraint0.SetCoefficient(y, 2)\n",
"\n",
" # Constraint 1: 3x - y >= 0.\n",
" constraint1 = solver.Constraint(0, solver.infinity())\n",
" constraint1.SetCoefficient(x, 3)\n",
" constraint1.SetCoefficient(y, -1)\n",
"\n",
" # Constraint 2: x - y <= 2.\n",
" constraint2 = solver.Constraint(-solver.infinity(), 2)\n",
" constraint2.SetCoefficient(x, 1)\n",
" constraint2.SetCoefficient(y, -1)\n",
" # [END constraints]\n",
"\n",
" # [START objective]\n",
" # Objective function: 3x + 4y.\n",
" objective = solver.Objective()\n",
" objective.SetCoefficient(x, 3)\n",
" objective.SetCoefficient(y, 4)\n",
" objective.SetMaximization()\n",
" # [END objective]\n",
"\n",
" # Solve the system.\n",
" # [START solve]\n",
" solver.Solve()\n",
" # [END solve]\n",
" # [START print_solution]\n",
" opt_solution = 3 * x.solution_value() + 4 * y.solution_value()\n",
" print('Number of variables =', solver.NumVariables())\n",
" print('Number of constraints =', solver.NumConstraints())\n",
" # The value of each variable in the solution.\n",
" print('Solution:')\n",
" print('x = ', x.solution_value())\n",
" print('y = ', y.solution_value())\n",
" # The objective value of the solution.\n",
" print('Optimal objective value =', opt_solution)\n",
" # [END print_solution]\n",
"\n",
"\n",
"LinearProgrammingExample()\n",
"# [END program]\n",
"\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}