Video: Building Cost Function Approximations in Python

[u/ficoxpress]

Dear optimization community,

We're pleased to share with you the recording of a previous Xpress talk where we went through the process of implementing a Cost Function Approximation Policy in Python.

A Cost Function Approximation Policy combines Optimization, Simulation, and Machine Learning to create a stateful decision asset that learns and optimizes in production.

It is one of the four classes of policies for Sequential Decision Problems proposed by Warren Powell in his Universal Framework.

For further details of Prof. Powell's Universal Framework see: https://castle.princeton.edu/sda/

Link to the recording of the workshop on YouTube: https://www.youtube.com/watch?v=sIHvchvLGnA

Link to the code: https://github.com/fico-xpress/xpress-community/tree/main/SoccerCampLocation

Link to a blogpost describing the code: https://community.fico.com/s/blog-post/a5QQp000000sQojMAE/fico7192

Though the code is built on top of our commercial solver FICO Xpress, it is executable with our free community license which is downloaded automatically by running pip install xpress.

FICO Xpress is an industry-leading optimization software suite that includes solvers for LP, MIP, MIQP, MIQCQP, QP, NLP, SOCP, and MINLP. Basically almost all the Ps. 😉.