Learning to approximate industrial problems by operations research classic problems

Abstract: Practitioners of operations research often consider difficult variants of well-known optimization problems, and struggle to find a good algorithm for their variants while decades of research have produced highly efficient algorithms for the well-known problems. We introduce a "machine learning for operations research" paradigm to build efficient heuristics for such variants of well-known problems. If we call the difficult problem of interest the hard problem, and the well known one the easy problem, we can describe our paradigm as follows. First, use a machine learning predictor to turn an instance of the hard problem into an instance of the easy one, then solve the instance of the easy problem, and finally retrieve a solution of the hard problem from the solution of the easy one. Using this paradigm requires to learn the predictor that transforms an instance of the hard problem into an instance of the easy one. We introduce two structured learning approaches to learn this predictor, and illustrate our paradigm and learning methodologies on several scheduling and path problems.

optimization and control

Audience: researchers in the topic

( paper )

---

Discrete Optimization Talks

Series comments: DOTs are virtual discrete optimization talks, organized by Aleksandr M. Kazachkov and Elias B. Khalil. To receive updates about upcoming DOTs, please join our mailing list. Topics of interest include theoretical, computational, and applied aspects of integer and combinatorial optimization.

The format is two thirty-minute talks and time for questions. Currently (January-April 2021), the seminars are scheduled on end-of-the-month Fridays at 1:00 p.m. ET. A special feature of DOTs is a social component. After a usual talk, you might grab a tea/coffee and chat with other attendees. Why not here too? Join us for some informal discussion after each DOT and throughout the week on our Discord channel.

---