Mathematical optimization is a powerful tool that can help businesses make better decisions, maximize efficiency, and improve outcomes. However, learning the ins and outs of optimization modeling can be a daunting task. That’s why we’ve created a variety of free resources designed to make learning mathematical optimization accessible, engaging, and fun. From interactive games to comprehensive guides, these resources cater to a wide range of learners, including students and data scientists who are new to the world of optimization.
|Introductory Videos and Jupyter Notebooks
If you haven’t already checked out the resources in the blog article, “Getting Started with Mathematical Optimization in Python,” we suggest you start there. In just minutes, you’ll be tinkering around inside the Gurobi Python interface, engaging with optimization models, and seeing how optimization and machine learning can work together. Once you’ve done that, come back here to take your learning to the next level.
Read the article, “Getting Started with Mathematical Optimization in Python.”
|Functional Code Examples
Gurobi’s Functional Code Examples dive into how to use Gurobi, across various programming languages, including C, C++, C#, Java, Visual Basic, and Python. The examples include reading a model from a file and building simple models, to implementing more complex applications such as workforce scheduling and the classic diet problem. They also demonstrate the use of specific features like multi-objective optimization, solution pools, and piecewise-linear objective functions.
View our Functional Code Examples library.
|Learning Through Play: The Burrito Optimization Game
The Burrito Optimization Game is an interactive, web-based game that introduces the concepts of mathematical optimization in a fun and engaging way. Players run a burrito shop and make decisions about what ingredients to buy, how many burritos to make, and what prices to set, with the goal of maximizing profit. The game comes with a comprehensive game guide and a teaching guide, making it a valuable resource for both individual learners and educators.
Watch the webinar, “Gamifying Optimization with the Burrito Optimization Game.”
|Coding Made Fun: The Gurobipy Card Game
The Gurobipy Card Game is another innovative tool that makes learning optimization modeling more approachable. The game consists of two sets of cards—one representing the elements of an optimization model and the other representing the modeling constructs and data structures used when building a model in Gurobi’s Python interface. The goal is to match each element card with the corresponding construct card, making it a fun and interactive way to learn the key elements of optimization modeling in Python.
Read the article, “Optimization Gamification: Introducing the Gurobipy Card Game.”
|OptiMods: Open-Source Repository of Optimization Use Cases
With Gurobi OptiMods, you can explore how optimization can be applied to solve real-world problems. Each OptiMods includes an example dataset and can be run using the free, limited-size Gurobi license that is automatically included in the Gurobi Python interface, gurobipy.
Read more about OptiMods in our press release.
Gurobi Machine Learning: Innovative Data Science Integration
To help data scientists explore the world of optimization, we created Gurobi Machine Learning—an open-source Python project to embed trained machine learning models directly into Gurobi. It allows you to add a trained machine learning model as a constraint to a Gurobi model (e.g., from scikit-learn, TensorFlow/Keras, or PyTorch). This way, you can easily connect your forecasting with optimization.
Watch the on-demand webinar, “Using Trained Machine Learning Predictors in Gurobi.”
|Optimization Application Demos
Our optimization application demos are helpful for demonstrating industry-specific use cases, including location planning, cutting stock, resource matching, and workforce scheduling. You just enter your sample information and watch the solver quickly output an answer. Keep in mind that businesses that try to solve these problems manually will need months of dedicated resources simply to generate a “good enough” solution. But with optimization, you can generate the best-possible solution in under a second.
View all of our application demos.
|Events and Webinars
Learn from the experts! We host live and virtual learning events regularly throughout the year.
Check out our Gurobi Events page to stay in-the-know!
With these free resources, Gurobi is making mathematical optimization more accessible than ever. If you’re just starting out with optimization, you might want to try our free-but-limited Gurobi for Online Courses license. It allows for only up to 2000 decision variables and 2000 constraints, but it’s enough power to tinker around with. Learn more about our Gurobi for Online Courses License.
Are you a current university student, faculty, or researcher? You may qualify to use Gurobi Optimizer at no cost. You’ll get the same features and performance that our commercial users enjoy, with no limits on model size, for free. Learn more about our Gurobi Academic License Program.
Choose the evaluation license that fits you best, and start working with our Expert Team for technical guidance and support.
Request free trial hours, so you can see how quickly and easily a model can be solved on the cloud.