Artificial Intelligence has seen tremendous advancements, with Generative AI taking the spotlight for its ability to create new, previously unseen outputs based on large amounts of training data. However, not all problems are best solved by generative methods. There are complex issues, often involving multiple possible solutions, where we need to determine the feasibility of a solution, and in-many cases, the optimal choice. In these instances, mathematical optimization is a better option.

This session is a software developers introduction to using mathematical optimization in Artificial Intelligence. In it, we will explore some of the foundational techniques for solving these types of problems and use open-source solvers to put them to work in our AI systems. Since this is a session for developers, we’ll keep it in terms that work best for us. That is, we’ll go heavy on the code and lighter on the math.