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Convergence rates of discrete-time stochastic approximation consensus algorithms: Graph-related limit bounds
Tang, Huaibin1,2; Li, Tao3
2018-02-01
Source PublicationSYSTEMS & CONTROL LETTERS
ISSN0167-6911
Volume112Pages:9-17
AbstractIn this paper, we study the convergence rates of the discrete-time stochastic approximation consensus algorithms over sensor networks with communication noises under general digraphs. Basic results of stochastic analysis and algebraic graph theory are used to investigate the dynamics of the consensus error, and the mean square and sample path convergence rates of the consensus error are both given in terms of the graph and noise parameters. Especially, calculation methods to estimate the mean square limit bounds are presented under balanced digraphs, and sufficient conditions on the network topology and the step sizes are given to achieve the fast convergence rate. For the sample path limit bounds, estimation methods are also presented under undirected graphs. (C) 2017 Elsevier B.V. All rights reserved.
KeywordConsensus Sensor network Martingale difference sequence Stochastic approximation Convergence rate
DOI10.1016/j.sysconle.2017.12.002
Language英语
Funding ProjectNational Natural Science Foundation of China[61522310] ; National Natural Science Foundation of China[61603215] ; China Postdoctoral Science Foundation[2016M601152] ; Young Scholars Program of Shandong University ; Shanghai Rising-Star Program[15QA1402000] ; Shu Guang project of Shanghai Municipal Education Commission ; Shanghai Education Development Foundation[17SG26]
WOS Research AreaAutomation & Control Systems ; Operations Research & Management Science
WOS SubjectAutomation & Control Systems ; Operations Research & Management Science
WOS IDWOS:000427213000002
PublisherELSEVIER SCIENCE BV
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Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29848
Collection中国科学院数学与系统科学研究院
Affiliation1.Shandong Univ, Sch Microelect, Jinan 250100, Shandong, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China
3.East China Normal Univ, Dept Math, Shanghai Key Lab Pure Math & Math Practice, Shanghai 200241, Peoples R China
Recommended Citation
GB/T 7714
Tang, Huaibin,Li, Tao. Convergence rates of discrete-time stochastic approximation consensus algorithms: Graph-related limit bounds[J]. SYSTEMS & CONTROL LETTERS,2018,112:9-17.
APA Tang, Huaibin,&Li, Tao.(2018).Convergence rates of discrete-time stochastic approximation consensus algorithms: Graph-related limit bounds.SYSTEMS & CONTROL LETTERS,112,9-17.
MLA Tang, Huaibin,et al."Convergence rates of discrete-time stochastic approximation consensus algorithms: Graph-related limit bounds".SYSTEMS & CONTROL LETTERS 112(2018):9-17.
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