Convergence of a stochastic subgradient method with averaging for nonsmooth nonconvex constrained optimization

被引：18
|
作者：
Ruszczynski, Andrzej [1 ]
机构：
[1] Rutgers State Univ, Dept Management Sci & Informat Syst, Piscataway, NJ 08854 USA
来源：
OPTIMIZATION LETTERS | 2020年 / 14卷 / 07期
关键词：
Stochastic subgradient method; Nonsmooth optimization; Generalized differentiable functions; Chain rule;
D O I：
10.1007/s11590-020-01537-8
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
We prove convergence of a single time-scale stochastic subgradient method with subgradient averaging for constrained problems with a nonsmooth and nonconvex objective function having the property of generalized differentiability. As a tool of our analysis, we also prove a chain rule on a path for such functions.
引用
收藏
页码：1615 / 1625
页数：11
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