GPU-Accelerated First-Order Methods for Mathematical Programming

10/31/24 | 4:15pm | E51-149

Sean Lu

Assistant Professor in Operations Research and Statistics
MIT

Abstract: In this talk, I will discuss the recent development of GPU-accelerated first-order methods for mathematical programming, particularly, linear programming, (convex) quadratic programming, and nonlinear programming. The talk will focus on algorithmic design, computational results, and theoretical understandings.

Bio: Haihao Lu is an Assistant Professor of Operations Research/Statistics at the MIT Sloan School of Management. Before joining MIT Sloan, he was an Assistant Professor at the University of Chicago Booth School of Business and a faculty researcher at Google Research’s large-scale optimization team. He obtained his PhD degree in Mathematics and Operations Research at MIT in 2019. His research primarily focuses on extending the computational and mathematical boundaries of methods for solving the large-scale optimization problems that arise in data science, machine learning, and operations research. His research has been recognized by several research awards, including Beale—Orachard-Hays Prize, INFORMS Revenue Management and Pricing Section Prize, INFORMS Optimization Society Young Researchers Prize, INFORMS Michael H. Rothkopf Junior Research Paper Prize (first place). Notably, the algorithms and software developed in his research have been utilized in leading technology companies and generated significant revenue impacts.