Colloquium: Discrete Optimization Meets Machine Learning

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:

  • https://papers.nips.cc/paper/2017/file/d9896106ca98d3d05b8cbdf4fd8b13a1-Paper.pdf
  • https://www.ekhalil.com/files/papers/KhaLebSonNemDil16.pdf
  • https://arxiv.org/abs/1811.06128