Gurobi Licenses and Resources for Academics

Mixed Integer Linear Programming Tutorial

In this 14-part video tutorial, Gurobi’s Sr. Technical Content Manager Pano Santos, PhD, explains the foundational principles of Mixed Integer Linear Programming. This series is useful for data scientists, computer scientists, business analysts, and systems/IT engineers who have some background in mathematical programming.

In this tutorial, you will learn:

• Why mixed-integer programming (MIP) is important.
• The advantages of using MIP instead of heuristics as a problem-solving approach.
• The basic methods for solving a MIP problem.

View the Tutorial

Linear Programming Tutorial

In this 14-part video tutorial, Gurobi’s Sr. Technical Content Manager Pano Santos, PhD, explains the foundational principles of Linear Programming and Mixed Integer Linear Programming. This series is useful for data scientists, computer scientists, business analysts, and systems/IT engineers who have some background in mathematical programming.

In this video series, you will learn about the key components to formulate Mixed Integer Linear Programming problems and the key principles of Linear Programming, which is the foundation of the entire field of mathematical optimization.

View the Tutorial

Optimization Application Demos

The new Gurobi Optimization Application demos illustrate the value of mathematical optimization. Each demo is essentially a proof-of-concept of an application that addresses a challenging and high-value problem of a particular industry. Gurobi Optimization Application Demos are deployed on Amazon Web Services using Docker and Gurobi Instant Cloud. These demos will give you the context to understand the problem you are solving before you dive into the modeling. You’ll also see how applications can be implemented within a modern IT architecture.

View the Optimization Application Demos here:

• Cell Tower Coverage
• Cutting Stock Problem with Multiple Master Rolls
• Facility Location
• Offshore Wind Farming
• Resource Matching Optimization
• The Traveling Salesman Problem
• Workforce Scheduling

Jupyter Notebook Modeling Examples

We’ve developed examples to give you a starting point to learn how to build your own models with our Jupyter Notebook Modeling.

These Jupyter Notebook modeling examples illustrate important features of the Gurobi Python API modeling objects, such as adding decision variables, building linear expressions, adding constraints, and adding an objective function for a mathematical optimization model. In addition, they explain more advanced features such as generalized constraints, piece-wise linear functions, multi-objective hierarchical optimization, as well as typical types of constraints such as allocation constraints, balance constraints, sequencing constraints, precedence constraints, etc. These modeling examples also show how the modeling objects of Gurobi and the typical type of constraints can be used in different contexts.

These modeling examples:

-Illustrate broad applicability of mathematical optimization.

-Show how to build mathematical optimization models.

-Are coded using the Gurobi Python API in Jupyter Notebook.

View the modeling examples here:

• Cell Tower Coverage
• Customer Assignment
• Facility Location
• HP Williams Modeling Examples
• L0-Regression
• Offshore Wind Farming
• Standard Pooling
• Traveling Salesman

Programming Language

While we support all major programming languages, most of our users choose our Python API for their modeling and development efforts. Even if you are currently familiar with another programming language, we have witnessed several new users being more productive using our Python API. You can learn more on our Gurobi Python Modeling and Development Environment page.

Documentation

Quick Start Guide

This guide covers software installation, how to obtain and setup a license, and an introduction to the Gurobi Interactive Shell. Download for:

• Windows in HTML or PDF.
• Linux in HTML or PDF.
• Mac OS X in HTML or PDF.

Example Tour

This guide gives an overview of the set of tasks you will likely want to perform with the Gurobi Optimizer, such as loading and solving a model, building and modifying a model, changing parameters, etc. It also contains a set of example code across a range of languages and all source code. You can view the PDF or the Online Guide.

Reference Manual

This manual contains documentation for the C, C++, C#, Java®, Microsoft® .NET, Python, MATLAB, and R interfaces, including sections on Attributes and Parameters. You can access the Reference Manual here.

Gurobi Cloud Guide

Learn how to use the Gurobi Cloud – a remote Gurobi service via cloud computing. The Gurobi Cloud allows you to run one or more Gurobi Compute Servers without having to purchase new computers or new Gurobi licenses. You can also use cloud instances as workers for distributed optimization. Before using the Gurobi Cloud, please familiarize yourself with Gurobi Remote Services. Available in HTML.

Gurobi Community Discussion Forum

In this moderated Gurobi Community Discussion Forum, users can read and post questions about the Gurobi Optimizer. You can also read current and past messages and knowledge base articles.