【Academic Seminar】Primal-dual algorithms for minimizing the sum of two or three functions
Title:Primal-dual algorithms for minimizing the sum of two or three functions
Speaker: Prof. Ming Yan,Michigan State University
Time and Date: 2:00 - 3:00 pm, Friday, Oct 12, 2018
Venue: Boardroom, Dao Yuan Building
There are several primal-dual algorithms for minimizing f(x)+g(x)+h(Ax), where f, g, and h are convex functions, f is differentiable with a Lipschitz continuous gradient, and A is a bounded linear operator. Some examples are Chambolle-Pock, Condat-Vu, Proximal Alternating Predictor-Corrector (PAPC), Primal-Dual Fixed-Point (PDFP), Asymmetric Forward-Backward-Adjoint splitting (AFBA), and Primal-Dual 3-Operator (PD3O). In this talk, I will discuss these primal-dual algorithms and their connections with alternating direction method of multipliers (ADMM). Then I will briefly give their applications in decentralized consensus optimization.
Ming Yan is an assistant professor in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics at Michigan State University. His research interests lie in computational optimization and its applications in image processing, machine learning, and other data-science problems. He received his B.S. and M.S in mathematics from University of Science and Technology of China in 2005 and 2008, respectively, and then Ph.D. in mathematics from University of California, Los Angeles in 2012. After completing his PhD, Ming Yan was a Postdoctoral Fellow in the Department of Computational and Applied Mathematics at Rice University from July 2012 to June 2013, and then moved to University of California, Los Angeles as a Postdoctoral Scholar and an Assistant Adjunct Professor from July 2013 to June 2015.