Try this Jupyter Notebook Modeling Example to learn how you can fit a function to a set of observations. We will formulate this regression problem as a linear programming problem using the Gurobi Python API and then solve it with the Gurobi Optimizer.
This model is example 11 from the fifth edition of Model Building in Mathematical Programming, by H. Paul Williams on pages 266 and 319-320.
This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models. The reader should also consult the documentation of the Gurobi Python API.
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 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 “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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