Machine Learning Seminar
Data-driven inverse linear optimization with noisy observations
Chemical Engineering and Materials Science
University of Minnesota
Wednesday, December 9, 2020
Online via zoom
Inverse optimization refers to the inference of unknown optimization models given decision data that are assumed to be optimal or near-optimal solutions to the unknown optimization problem. In this work, we consider data-driven inverse linear optimization, where the underlying decision-making process can be modeled as a linear program whose cost vector is unknown. We propose a two-phase algorithm to determine the best estimate of the cost vector given noisy observations. Moreover, we propose an efficient decomposition algorithm to solve large instances of the problem. The algorithm extends naturally to an online learning environment where it can be used to provide quick updates of the cost estimate as new data becomes available over time. For the online setting, we further develop an effective adaptive sampling strategy that guides the selection of the next samples. The efficacy of the proposed methods is demonstrated in computational experiments involving two applications, customer preference learning and cost estimation for production planning. The results show significant reductions in computation and sampling efforts.
Qi Zhang is an Assistant Professor in the Department of Chemical Engineering and Materials Science at the University of Minnesota. He received his Ph.D. in Chemical Engineering from Carnegie Mellon University and worked at BASF prior to joining the University of Minnesota. His research lies at the intersection of chemical engineering and operations research, particularly focusing on mixed-integer optimization, decision making under uncertainty, and data analytics, with applications in sustainable energy and process systems, advanced manufacturing, and supply chain management.