报告题目： Primal-dual algorithms for the sum of two and three functions

报告人： Ming Yan,  CMSE/Math, Michigan State University

报告时间：12月20号下午2:30--3:30

报告地点：1208

摘要： 

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. Two examples for minimizing the sum of two functions are Chambolle-Pock (f=0) and Proximal Alternating Predictor-Corrector (PAPC) (g=0). In this talk, I will introduce a new primal-dual algorithm for minimizing the sum of three functions. This new algorithm has the Chambolle-Pock and PAPC as special cases. It also enjoys most advantages of existing algorithms for solving the same problem. In addition, I will show that the parameters for PAPC can be relaxed. Then I will give some 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.