Colloquium: Discrete Optimization Meets Machine Learning
Discrete Optimization algorithms underlie intelligent decision-making in a wide variety of domains. From airline fleet scheduling to kidney exchanges and data center resource management, decisions are often modeled with binary on/off variables that are subject to operational and financial constraints.
In this talk, I introduce “Data-Driven Algorithm Design”, a novel paradigm for boosting the performance of discrete optimization algorithms by leveraging two types of data: the set of problem instances arising from the application of interest; and information generated while solving each instance. I will present Machine Learning (ML) approaches that have advanced the state-of-the-art in both exact integer programming solvers as well as heuristic algorithms, and discuss opportunities for future research in this growing area.
Some relevant papers: