SPEAKER: Prof. Xiaojing Ye, Georgia State University

TIME: 3:00-4:00pm, 2014-6-26

VENUE: Room 82J04, Jia Yi Bing Building, 82 Jing Chun Yuan, BICMR

Abstract: We introduce several variations of classical optimization methods using adaptive searches of step sizes, and show that these can significantly improve the efficiency of traditional approaches on large-scale nonconvex or nonsmooth inverse problems such as those in imaging applications. For instance, when the Barzilai-Borwein step size selection method and a properly designed line search strategy are adopted, we can solve a large class of nonsmooth optimization problems with low per-iteration cost and obtain extensively accelerated convergence. The promising performance is further demonstrated using large real-world imaging data sets.

Short Bio: Dr. Xiaojing Ye is currently an assistant professor of mathematics at Georgia State University, USA. He received bachelor degree of mathematics from Peking University in China, and master degree of statistics and doctoral degree of mathematics both from University of Florida in USA. His research interests are PDE-based image analysis, numerical optimization, stochastic modeling and computations.