Discrete Optimization Talks (DOTs)
DOTs are virtual discrete optimization talks, organized by Aleksandr M. Kazachkov and Elias B. Khalil. To receive updates (and the Zoom link) for upcoming DOTs, please join our mailing list. If you would like to give a DOT, please fill out this form or email us.
Topics of interest include theoretical, computational, and applied aspects of integer and combinatorial optimization.
The format is two 30-minute talks each session. From September to December 2022, the seminars are scheduled one Friday of every month 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, as well as throughout the week on our Discord channel.
Videos of past DOTs are posted under Past Talks and can also be found on our YouTube Channel.
Join the mailing list to receive information about our upcoming seminars and how to virtually participate. Joining this list is necessary to receive the password for each DOT (usually sent on the Monday of the week in which there are DOTs). You may also wish to subscribe to our Google calendar (also available in ics format).
Upcoming Talks
MIP-based branch-and-bound for the discrete ordered median problem
Abstract: Given a set N of n points, a non-negative distance matrix D of dimensions n x n, an integer p > 1 and an integer-valued weight vector lambda of size n, we consider the problem of selecting a subset C of exactly p points from N (also referred to as the centres) so as to: 1) assign each point in N to its closest centre in C; 2) sort the resulting distances (between every point and its center) from smallest to largest in a vector that we denote d*; 3) minimize <lambda, d*>. This problem generalizes several classical location problems such as the vertex p-centre problem or the p-median problem. We present a method that decomposes the problem in a series of simpler problems with unit weights to derive a valid dual bound. The subproblems happen to be much easier both theoretically and in practice than the DOMP. We embed the dual bound within a branch-and-bound solver for the DOMP. We present computational results to assess the performance of the proposed approach.
TBD
For questions or suggestions, please contact Aleksandr M. Kazachkov or Elias B. Khalil.
DOTs are supported by the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making and the University of Florida Department of Industrial and Systems Engineering.
