Discrete Optimization Talks (DOTs)
Discrete Optimization Talks (DOTs) is a virtual seminar series from the Mixed Integer Programming Society (MIPS), a technical section of the Mathematical Optimization Society.
DOTs are virtual discrete optimization talks, organized by Silvia Di Gregorio, 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 January to April 2024, the seminars are scheduled the last Friday of every month at 12:00 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.
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).
Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization
Abstract: We consider the problem of optimizing a sequence of mixed-integer linear optimization problems (MIPs) with varying parameters. By combining recent advances in cutting plane generation and differentiation through convex optimization problems, we construct a new differentiable architecture to predict the optimal cutting planes from the key parameters of each instance. During the offline phase, our method maximizes the cutting planes’ efficiency by evaluating the derivative of the solution of the continuous relaxations with respect to the cut-generating parameters. We show on preliminary computational results that, once trained, our architecture computes solutions with low infeasibility and suboptimality with fast and predictable execution times.
Biography: Bartolomeo Stellato is an Assistant Professor in the Department of Operations Research and Financial Engineering at Princeton University. Previously, he was a Postdoctoral Associate at the MIT Sloan School of Management and Operations Research Center. He received a DPhil (PhD) in Engineering Science from the University of Oxford, a MSc in Robotics, Systems and Control from ETH Zürich, and a BSc in Automation Engineering from Politecnico di Milano. He is the developer of OSQP, a widely used solver in mathematical optimization. Bartolomeo Stellato's awards include the NSF CAREER Award, the Franco Strazzabosco Young Investigator Award from ISSNAF, the Princeton SEAS Innovation Award in Data Science, the Best Paper Award in Mathematical Programming Computation, and the First Place Prize Paper Award in IEEE Transactions on Power Electronics. His research focuses on data-driven computational tools for mathematical optimization, machine learning, and optimal control.
Machine-learning augmented branch-and-bound for mixed-integer linear optimization
Abstract: Mixed-integer linear optimization solvers use branch and bound as their main component. In recent years, there has been an explosive development in the use of machine learning algorithms for enhancing all main tasks involved in the branch-and-bound algorithm, such as primal heuristics, branching, cutting planes, node selection and solver configuration decisions. In this talk we mention a selection of results in this area together with some relations between integer linear optimization and deep learning. The talk is based on a joint paper with Lara Scavuzzo Montana, Andrea Lodi, and Neil Yorke-Smith.
Biography: Karen Aardal holds a position of full professor of optimization at Delft University of Technology. Her research interests include integer optimization algorithms, approximation algorithms, applications of machine learning, and the modeling and resolution of problems within energy and health care. She is a former chair of the Mathematical Optimization Society, and has served on the board of the INFORMS Computing Society in three periods. She served on several editorial boards, such as Math Programming Series B, INFORMS J on Computing, and OR Letters. In the Netherlands she is serving on the board of domain Science of the Dutch Research Council NWO.
IRELAND: Iterative Rule Extension for the Logical ANalysis of Data
Abstract: Data-driven decision making is rapidly gaining popularity, fuelled by the ever-increasing amounts of available data and encouraged by the development of models that can identify non-linear input-output relationships. Simultaneously, the need for interpretable prediction- and classification methods is increasing, as this improves both our trust in these models and the amount of information we can abstract from data. An important aspect of this interpretability is to obtain insight in the sensitivity-specificity trade-off constituted by multiple plausible input-output relationships. These are often shown in a receiver operating characteristic curve. These developments combined lead to the need for a method that can identify complex yet interpretable input-output relationships from large data, i.e., data containing large numbers of samples and features. Boolean phrases in disjunctive normal form (DNF) are highly suitable for explaining nonlinear input-output relationships in a comprehensible way. Mixed integer linear programming can be used to obtain these Boolean phrases from binary data, though its computational complexity prohibits the analysis of large datasets. This work presents IRELAND, an algorithm that allows for abstracting Boolean phrases in DNF from data with up to 10,000 samples and features. The results show that for large datasets IRELAND outperforms the current state-of-the-art in terms of prediction accuracy. Additionally, by construction, IRELAND allows for an efficient computation of the sensitivity-specificity trade-off curve, allowing for further understanding of the underlying input-output relationship.
Bio: Marleen Balvert works on optimization and data science problems that have a direct societal impact. Her research aims to answer questions that arise from other fields, such as bioinformatics and food security. As a researcher at the Zero Hunger Lab, she uses data analytics and optimization to help NGOs achieve Sustainable Development Goal 2: zero hunger. Furthermore, she works on the development of data analysis techniques for large genome data. The goal of these methods is to identify those genetic characteristics that cause individual traits, such as susceptibility to genetic diseases for humans or crop traits such as flavor and yield. Interdisciplinary collaborations are central to her work.