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Don’t Go It Alone. Gurobi and Its Partners Provide the Continuum of Support You Need.

While the mathematical optimization field is more than 70 years old, many customers are still learning how to make the most of its capabilities. That’s why, at Gurobi, we have established the Gurobi Alliance partner network—a group of trusted partners who can support you in achieving your optimization goals.

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Free Udemy Course: Intro to Optimization

Unlock the power of optimized decision-making with this online course developed by Gurobi in partnership with Dr. Joel Sokol, professor at Georgia Tech.

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Gurobi Finance to Enhance Financial Decision-Making

New technical documentation will help finance professionals leverage MIP technology for advanced portfolio optimization.

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Jupyter Notebook Model

Best Feature Selection for Forecasting

A linear regression problem that minimizes the residual sum of squares subject to the constraint that the number of non-zero feature weights should be less than or equal to a given upper limit.

In this example, you will learn how to perform linear regression with feature selection using mathematical programming. We’ll show you how to construct a mixed-integer quadratic programming (MIQP) model of this linear regression problem, implement this model in the Gurobi Python API, and generate an optimal solution.

This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you should have some knowledge about building mathematical optimization models.

Access the Jupyter Notebook Modeling Example

Click on the button below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.

How to Run the Jupyter Notebook Modeling Example

To run the example the first time, choose “Runtime” and then click “Run all”.
All the cells in the Jupyter Notebook will be executed.
The example will install the gurobipy package, which includes a limited Gurobi license that allows you to solve small models.
You can also modify and re-run individual cells.
For subsequent runs, choose “Runtime” and click “on “Restart and run all”.
The Gurobi Optimizer will find the optimal solution of the modeling example.

Check out the Colab Getting Started Guide for full details on how to use Colab Notebooks as well as create your own.

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Best Feature Selection for Forecasting

Access the Jupyter Notebook Modeling Example

How to Run the Jupyter Notebook Modeling Example

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