Adaptive Stochastic Mirror Descent for Constrained Optimization

被引：0
|
作者：
Bayandina, Anastasia [1 ]
机构：
[1] Moscow Inst Phys & Technol, Moscow, Russia
来源：
2017 CONSTRUCTIVE NONSMOOTH ANALYSIS AND RELATED TOPICS (DEDICATED TO THE MEMORY OF V.F. DEMYANOV) (CNSA) | 2017年
基金：
俄罗斯科学基金会;
关键词：
D O I：
暂无
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
Mirror Descent (MD) is a well-known method of solving non-smooth convex optimization problems. This paper analyzes the stochastic variant of MD with adaptive stepsizes. Its convergence on average is shown to be faster than with the fixed stepsizes and optimal in terms of lower bounds.
引用
收藏
页码：40 / 43
页数：4
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