报告题目：Studying the linear convergence of ADMM for structured convex optimization via variational analysis
报告人：张进
报告人单位：南方科技大学数学系
时间：2019年4月18日15:30
地点：教2-314室
主办单位：bat365在线官网入口
报告内容：In this talk, we systematically study the linear convergence of ADMM in the context of convex optimization through the lens of variaitonal analysis. We show that the linear convergence of ADMM can be guaranteed without the strong convexity of objective functions together with the full rank assumption of the coefficient matrices, or the full polyhedricity assumption of their subdifferential; and it is possible to discern the linear convergence for various concrete applications, especially for some representative models arising in statistical learning. We use some variational analysis techniques sophisticatedly; and our analysis is conducted in the most general proximal version of ADMM with Fortin and Glowinski's larger step size so that all major variants of the ADMM known in the literature are covered.
报告人简介：张进，南方科技大学数学系助理教授。2007年于大连理工大学人文社会科学学院获文学学士，2010年于大连理工大学数学科学学院获理学硕士学位，2014年12月于加拿大维多利亚大学数学与统计系获应用数学博士学位。2015年4月至2019年1月就职香港浸会大学数学系，2019年1月至今就职南方科技大学。张进博士主要从事最优化理论与方法研究，发表SCI检索论文20余篇，其中多篇论文发表在 Mathematical Programming，SIAM Journal on Optimization，SIAM journal on Numerical Analysis等应用数学领域顶级期刊上。