{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": {} }, "source": [ "\n", "*This notebook contains course material from [CBE40455](https://jckantor.github.io/CBE40455) by\n", "Jeffrey Kantor (jeff at nd.edu); the content is available [on Github](https://github.com/jckantor/CBE40455.git).\n", "The text is released under the [CC-BY-NC-ND-4.0 license](https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode),\n", "and code is released under the [MIT license](https://opensource.org/licenses/MIT).*" ] }, { "cell_type": "markdown", "metadata": { "pycharm": {} }, "source": [ "\n", "< [Getting Started with CVXPY](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/01.01-Getting-Started-with-CVXPY.ipynb) | [Contents](toc.ipynb) | [Getting Started with GNU MathProg in Jupyter Notebooks](http://nbviewer.jupyter.org/github/jckantor/CBE40455/blob/master/notebooks/01.03-Getting-Started-with-GNU-MathProg.ipynb) >
" ] }, { "cell_type": "markdown", "metadata": { "pycharm": {} }, "source": [ "# Getting Started with Gurobi\n", "\n", "[Gurobi](http://www.gurobi.com) is a commercial, state-of-the-art mathematical programming engines used in a diverse array of industries. It is available under academic licensing terms that allow free use by faculty and students in accredited insitutions, and comes with a very complete Python interface. The purpose of this notebook is to help you get started using Gurobi via the Python interface.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": {} }, "source": [ "## Simple Two-Variable LP" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "pycharm": {} }, "outputs": [ { "data": { "text/plain": [ "[2.4, 1.6]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from gurobi import *\n", "m = Model()\n", "\n", "v0 = m.addVar()\n", "v1 = m.addVar()\n", "m.update()\n", "\n", "m.getVars()\n", "\n", "m.addConstr(v0 - v1 <= 4)\n", "m.addConstr(v0 + v1 <= 4)\n", "m.addConstr(-0.25*v0 + v1 <= 1)\n", "m.setObjective(v1, GRB.MAXIMIZE)\n", "m.params.outputflag = 0\n", "m.optimize()\n", "[v0.x,v1.x]" ] }, { "cell_type": "markdown", "metadata": { "pycharm": {} }, "source": [ "## Assignment Problem" ] }, { "cell_type": "markdown", "metadata": { "pycharm": {} }, "source": [ "### Problem Data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "pycharm": {} }, "outputs": [ { "data": { "text/html": [ "
\n", " | Atlanta | \n", "Boise | \n", "Charlotte | \n", "Dallas | \n", "Fresno | \n", "
---|---|---|---|---|---|
Austin | \n", "921 | \n", "1627 | \n", "1166 | \n", "196 | \n", "1594 | \n", "
Boston | \n", "1078 | \n", "2661 | \n", "837 | \n", "1767 | \n", "3107 | \n", "
Chicago | \n", "716 | \n", "1693 | \n", "756 | \n", "925 | \n", "2140 | \n", "
Denver | \n", "1400 | \n", "815 | \n", "1561 | \n", "788 | \n", "1142 | \n", "
Edmonton | \n", "3764 | \n", "1718 | \n", "3848 | \n", "3310 | \n", "2835 | \n", "
" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }