Discrete Optimization Talks (DOTs)
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. 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 a weekly session with two 30-minute talks. Currently, the seminars are scheduled on Tuesdays at 2:00 p.m. ET. A special feature of DOTs will be 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.
Videos of past DOTs are posted in the Past Talks section on this page.
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. You may also wish to subscribe to our Google calendar (also available in ics format).
Week of June 8, 2020
Tuesday, June 16, 2020 at 2:00 p.m. ET
Tuesday, June 23, 2020 at 2:00 p.m. ET
Beginning in the 1960s, techniques from operations research began to be used to generate political districting plans. A classical example is the integer programming model of Hess et al. (Operations Research 13(6):998--1006, 1965). Due to the model's compactness-seeking objective, it tends to generate contiguous or nearly-contiguous districts, although none of the model's constraints explicitly impose contiguity. Consequently, Hess et al. had to manually adjust their solutions to make them contiguous. Since then, there have been several attempts to adjust the Hess model and other models so that contiguity is explicitly ensured. In this talk, we review two existing models for imposing contiguity, propose two new ones, and analytically compare them in terms of their strength and size. We conduct an extensive set of numerical experiments to evaluate their performance. While many believe that contiguity constraints are particularly difficult to deal with, we find that the problem does not become harder when contiguity is imposed. In fact, a branch-and-cut implementation of a cut-based model generates, for the first time, optimally compact districting plans for 21 different US states at the census tract level (under the compactness objective proposed by Hess et al.). To encourage future research in this area, and for purposes of transparency, we make our test instances and source code publicly available.
Tuesday, June 30, 2020
Learning to Scale
Confirmed Speakers, Date TBD
June 2, 2020:
Andrés Gómez (USC), Outlier detection in time series via mixed-integer conic quadratic optimization
May 5, 2020:
April 21, 2020: