Optimal distributed stochastic mirror descent for strongly convex optimization

doi:10.1016/j.automatica.2017.12.053

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 系统科学研究所

	Optimal distributed stochastic mirror descent for strongly convex optimization
	Yuan, Deming 1,2; Hong, Yiguang3 ; Ho, Daniel W. C.4; Jiang, Guoping 2
刊名	AUTOMATICA
	2018-04-01
卷号	90 页码:196-203
关键词	Distributed stochastic optimization Strong convexity Non-Euclidean divergence Mirror descent Epoch gradient descent Optimal convergence rate
ISSN号	0005-1098
DOI	10.1016/j.automatica.2017.12.053
英文摘要	In this paper we consider convergence rate problems for stochastic strongly-convex optimization in the non-Euclidean sense with a constraint set over a time-varying multi-agent network. We propose two efficient non-Euclidean stochastic subgradient descent algorithms based on the Bregman divergence as distance-measuring function rather than the Euclidean distances that were employed by the standard distributed stochastic projected subgradient algorithms. For distributed optimization of non-smooth and strongly convex functions whose only stochastic subgradients are available, the first algorithm recovers the best previous known rate of O(ln(T)/T) (where T is the total number of iterations). The second algorithm is an epoch variant of the first algorithm that attains the optimal convergence rate of O(1/T), matching that of the best previously known centralized stochastic subgradient algorithm. Finally, we report some simulation results to illustrate the proposed algorithms. (C) 2018 Elsevier Ltd. All rights reserved.
资助项目	Natural Science Fund for Excellent Young Scholars of Jiangsu Province[BK20170099] ; National Natural Science Foundation of China[61573344] ; National Natural Science Foundation of China[61733018] ; National Natural Science Foundation of China[61374180] ; Research Grants Council of the Hong Kong Special Administrative Region, China[CityU 11300415]
WOS研究方向	Automation & Control Systems ; Engineering
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000427217600022
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/29755]
专题	系统科学研究所
通讯作者	Yuan, Deming
作者单位	1.Nanjing Univ Sci & Technol, Sch Automat, Nanjing 210094, Jiangsu, Peoples R China 2.Nanjing Univ Posts & Telecommun, Coll Automat, Nanjing 210023, Jiangsu, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China 4.City Univ Hong Kong, Dept Math, Kowloon, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Deming,Hong, Yiguang,Ho, Daniel W. C.,et al. Optimal distributed stochastic mirror descent for strongly convex optimization[J]. AUTOMATICA,2018,90:196-203.
APA	Yuan, Deming,Hong, Yiguang,Ho, Daniel W. C.,&Jiang, Guoping.(2018).Optimal distributed stochastic mirror descent for strongly convex optimization.AUTOMATICA,90,196-203.
MLA	Yuan, Deming,et al."Optimal distributed stochastic mirror descent for strongly convex optimization".AUTOMATICA 90(2018):196-203.