Title: 

GPU-Accelerated Linear Programming and Beyond

Abstract: 

The rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This talk aims to provide an overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLPx. The extensions to GPU-based optimization beyond LP, including convex quadratic programming and semidefinite programming, will also be discussed.

Bio:

 Jinwen Yang is a final-year Ph.D. student at the University of Chicago, advised by Professor Haihao Lu. His research interests are in optimization, with a particular focus on optimization algorithms tailored to modern hardware (like GPUs) and intended for practical applications. He obtained B.S. in Mathematics and Applied Mathematics from Fudan University.