Discover learning content designed to help you jump-start your education in using optimization.

We suggest you start with the Linear Programming Tutorial, which provides a strong foundation for linear programming and mixed-integer linear programming. Or you can dive right in and start using the Code Examples and Jupyter Notebook Modeling Examples.

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. You will learn why mixed-integer programming (MIP) is important, methods for solving a MIP problem, the advantages of using MIP instead of heuristics, and more.

View the Tutorial

 


Getting Started with Gurobi

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, you may find that – like many of our other users – you are more productive using our Python API. You can learn more on our Gurobi Python Modeling and Development Environment page.

 

Quick Start Guide

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

 

Modeling Basics, LP and MIP Overviews

The Modeling Basics page offers an overview of the modeling process – from creating the conceptual model, to implementing, and finally solving it. The LP Basics and MIP Basics pages provide an overview of each problem type and highlight the algorithms used to solve each.

Example Tour

This guide takes you through 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

In this guide, you will 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.

Guidance for Your Journey

Gurobi: Always Free for Academics

We make it easy for students, faculty, and researchers to work with mathematical optimization.

Trusted Partners, at Your Service

When you face complex optimization challenges, you can trust our Gurobi Alliance partners for expert services.

We’ve Got Your Back

Our global team of helpful, PhD-level experts are here to support you—with responses in hours, not days.

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News
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Event
Webinar: What’s New in Gurobi 10.0
In this webinar, attendees will get a first look at our upcoming product release, Gurobi 10.0. We will summarize the performance improvements and highlight some of the underlying algorithmic advances, such as the network simplex algorithm, enhancements in concurrent LP, and optimization based bound tightening.
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