Welcome

This brief guide is designed for MIT Sloan students enrolled in 15.071: The Analytics Edge (Spring 2022). The guide serves as an introduction to installing and using the Gurobi Optimizer within the Python programming environment.

Please read this documentation carefully and consult it frequently. Course recitations and other TA support complement this guide.

Contents

• 1. Installation
  • 1.1. Installing Python
    • 1.1.1. Installing Anaconda
    • 1.1.2. Testing the installation
  • 1.2. Installing Gurobi
    • 1.2.1. Registering for an academic license
    • 1.2.2. Downloading the software
    • 1.2.3. Obtaining and activating the academic license
    • 1.2.4. Installing gurobipy (Gurobi’s Python API) in Anaconda
• 2. Using Notebooks
  • 2.1. What is a Jupyter Notebook?
  • 2.2. Launching a Jupyter Notebook
  • 2.3. Basic structure
  • 2.4. Shortcuts
  • 2.5. Getting help
• 3. Gurobi Tutorial
  • 3.1. Illustrative Example
  • 3.2. Model I
    • 3.2.1. Step 1: Load the gurobipy library
    • 3.2.2. Step 2: Initialize the model object
    • 3.2.3. Step 3: Add decision variables
    • 3.2.4. Step 4: Add constraints
    • 3.2.5. Step 5: Set objective function
    • 3.2.6. Step 6: Solve
    • 3.2.7. Step 7: Save the solution
    • 3.2.8. Model I complete code sample
  • 3.3. Model II
  • 3.4. Model III
  • 3.5. Model IV
  • 3.6. Model V
  • 3.7. Model VI
• 4. Using Colab and Keras for Neural Networks
  • 4.1. Using Colab
  • 4.2. Using Keras
  • 4.3. Class disclaimer on the tools used
• 5. Python Essentials
  • 5.1. Basic Data Types
  • 5.2. Working with DataFrames using Pandas
  • 5.3. Flow Control
  • 5.4. Installing and Importing Packages
  • 5.5. Trivia
• 6. Resources
  • 6.1. Gurobi resources
  • 6.2. Python resources