147 lines
4.3 KiB
Plaintext
147 lines
4.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "google",
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"metadata": {},
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"source": [
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"##### Copyright 2025 Google LLC."
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]
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},
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{
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"cell_type": "markdown",
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"id": "apache",
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"metadata": {},
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"source": [
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"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"you may not use this file except in compliance with the License.\n",
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"You may obtain a copy of the License at\n",
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"\n",
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" http://www.apache.org/licenses/LICENSE-2.0\n",
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"\n",
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"Unless required by applicable law or agreed to in writing, software\n",
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"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
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"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"See the License for the specific language governing permissions and\n",
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"limitations under the License.\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "basename",
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"metadata": {},
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"source": [
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"# linear_programming_example"
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]
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},
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{
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"cell_type": "markdown",
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"id": "link",
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"metadata": {},
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"source": [
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"<table align=\"left\">\n",
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"<td>\n",
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"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/main/examples/notebook/linear_solver/linear_programming_example.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
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"</td>\n",
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"<td>\n",
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"<a href=\"https://github.com/google/or-tools/blob/main/ortools/linear_solver/samples/linear_programming_example.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\"/>View source on GitHub</a>\n",
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"</td>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "doc",
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"metadata": {},
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"source": [
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"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "install",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install ortools"
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]
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},
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{
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"cell_type": "markdown",
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"id": "description",
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"metadata": {},
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"source": [
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"\n",
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"Linear optimization example."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ortools.linear_solver import pywraplp\n",
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"\n",
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"\n",
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"\n",
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"def LinearProgrammingExample():\n",
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" \"\"\"Linear programming sample.\"\"\"\n",
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" # Instantiate a Glop solver, naming it LinearExample.\n",
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" solver = pywraplp.Solver.CreateSolver(\"GLOP\")\n",
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" if not solver:\n",
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" return\n",
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"\n",
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" # Create the two variables and let them take on any non-negative value.\n",
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" x = solver.NumVar(0, solver.infinity(), \"x\")\n",
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" y = solver.NumVar(0, solver.infinity(), \"y\")\n",
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"\n",
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" print(\"Number of variables =\", solver.NumVariables())\n",
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"\n",
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" # Constraint 0: x + 2y <= 14.\n",
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" solver.Add(x + 2 * y <= 14.0)\n",
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"\n",
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" # Constraint 1: 3x - y >= 0.\n",
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" solver.Add(3 * x - y >= 0.0)\n",
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"\n",
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" # Constraint 2: x - y <= 2.\n",
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" solver.Add(x - y <= 2.0)\n",
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"\n",
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" print(\"Number of constraints =\", solver.NumConstraints())\n",
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"\n",
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" # Objective function: 3x + 4y.\n",
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" solver.Maximize(3 * x + 4 * y)\n",
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"\n",
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" # Solve the system.\n",
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" print(f\"Solving with {solver.SolverVersion()}\")\n",
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" status = solver.Solve()\n",
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"\n",
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" if status == pywraplp.Solver.OPTIMAL:\n",
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" print(\"Solution:\")\n",
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" print(f\"Objective value = {solver.Objective().Value():0.1f}\")\n",
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" print(f\"x = {x.solution_value():0.1f}\")\n",
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" print(f\"y = {y.solution_value():0.1f}\")\n",
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" else:\n",
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" print(\"The problem does not have an optimal solution.\")\n",
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"\n",
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" print(\"\\nAdvanced usage:\")\n",
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" print(f\"Problem solved in {solver.wall_time():d} milliseconds\")\n",
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" print(f\"Problem solved in {solver.iterations():d} iterations\")\n",
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"\n",
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"\n",
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"LinearProgrammingExample()\n",
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"\n"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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