腾讯会议：507 103 480
邀请人：徐玲玲副教授
报告摘要： Recently, we have proposed using convex combination technique to construct new primal-dual full splitting algorithms for solving some structured convex optimization problems. In this talk, I will review the proposed algorithms and their convergence properties. A connection to the primal-dual algorithm of Chambolle and Pock will also be given. we have proposed a golden ratio primal dual algorithm (GRPDA) for solving structured convex optimization problems and it can be viewed as a new adaptation of the classical Arrow-Hurwicz method using a convex combination technique. The convex combination technique has been applied to several related problems.
报告人简介： Junfeng Yang, Professor, Department of Mathematics, Nanjing University. He is mainly interested in designing, analyzing and implementing robust and efficient algorithms for solving optimization problems arising from signal and image processing, compressive sensing and sparse optimization, and so on. Together with collaborators, he has developed Matlab packages FTVd for image deblurring and YALL1 for L1 problems in compressive sensing.