Gurobi is committed to supporting the Academic community in the teaching and use of optimization. Gurobi supports teaching, research, and real-time use of optimization solvers in academic institutions by providing free Gurobi software licenses that are no-size-limit and full-featured versions. Students, researchers, teachers, and fresh graduates are eligible to get their free licenses. Gurobi also offers a version for use by students taking online courses, such as those offered by Coursera. Academia can easily install Gurobi software and licenses for individual computers or institution sites.
Gurobi has pulled together a number of technical resources to help you learn how to use optimization.
Free Gurobi Licenses for Academics and Researchers
The free licenses we provide have all the features and performance of the full Gurobi Optimizer, with no limits on model size. You can get up and running in minutes with an individual academic license or an educational institution site license.
Take Gurobi with You for Graduates
Our goal is to help graduating students continue to use Gurobi as they move from their academic studies to a commercial environment. Take advantage of this free, one-year license offer as you graduate and move into a professional role.
Gurobi has a number of new tutorials, Optimization Application Demos and Jupyter Notebook modeling examples to help you broaden your knowledge of Optimization.
Overview: Mixed Integer Linear Programming Tutorial
View this video to get a preview of the Mixed Integer Linear Programming Tutorial.
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
Overview: Linear Programming – An Introduction
Watch this video to get a preview of the Linear Programming 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:
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: