科学研究
学术报告
On the Linear Convergence to Weak/Standard D-Stationary Points of DCA-Based Algorithms for Structured Non-Smooth DC Programming
发布时间:2021-09-02浏览次数:

题目:On the Linear Convergence to Weak/Standard D-Stationary Points of DCA-Based Algorithms for Structured Non-Smooth DC Programming

报告人:陶敏 教授 (南京大学) 

地点:腾讯会议室

时间:2021年9月2日 14:00-15:00

摘要:We consider a class of structured nonsmooth difference-of-convex minimization. We allow non-smoothness in both the convex and concave components in the objective function, with a finite max structure in the concave part. Our focus is on algorithms that compute a (weak or standard) directional-stationary point as advocated in a recent work of Pang et al. (Math Oper Res 42:95–118, 2017). Our linear convergence results are based on direct generalizations of the assumptions of error bounds and separation of isocost surfaces proposed in the seminal work of Luo and Tseng (Ann Oper Res 46–47:157–178, 1993), as well as one additional assumption of locally linear regularity regarding the intersection of certain stationary sets and dominance regions.

An interesting by-product is to present a sharper characterization of the limit set of the basic algorithm proposed by Pang et al., which fits between d-stationarity and global optimality. We also discuss sufficient conditions under which these assumptions hold.

Finally, we provide several realistic and nontrivial statistical learning models where all assumptions hold.

腾讯会议:https://meeting.tencent.com/dm/JhgT1PReGNuT 

会议 ID:767 357 912

All are welcome!