How to Solve Optimization Problems with Python

How to Solve Optimization Problems with Python

How to use the PuLP library to solve Linear Programming problems with a few lines of code

Linear programming (or linear optimization) is the process of solving for the best outcome in mathematical problems with constraints. PuLP is a powerful library that helps Python users solve these types of problems with just a few lines of code.

I have found that PuLP is the simplest library for solving these types of linear optimization problems. The objective function and constraints can all be added in an interesting layered approach with just one line of code each. This means that we can spend less time coding and more time solving the problem. The documentation is also easily readable and includes five easy to follow case studies.

In this article, we will use daily fantasy sports (DFS) data from Fanduel to demonstrate how to solve a maximization problem with multiple constraints. The intention is that these steps will be generalizable to other problems you would like to solve.

We will be working with DFS data because it allows us to walk through the entire process from understanding a real-world problem to defining the problem in terms of an objective function and constraints, to finally coding a solution in Python. All of these steps are an important part of any linear programming problem. DFS is a simple enough context to understand these steps while still being complex enough to allow for discussion about them.

If you would like to follow along, the data is freely available by following the steps below:

  1. Create a free fantasy account at Fanduel
  2. Go to the NBA tab within the Lobby
  3. Click on any of the contests below and click on the “enter new lineup” button
  4. Finally, click on “Download Player List” at the top of the page to get the data as a csv file

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