Loading…
Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging
This paper introduces a post-iteration averaging algorithm to achieve asymptotic optimality in convergence rates of stochastic approximation algorithms for consensus control with structural constraints. The algorithm involves two stages. The first stage is a coarse approximation obtained using a seq...
Saved in:
Published in: | Journal of control theory and applications 2013-02, Vol.11 (1), p.1-9 |
---|---|
Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c2971-db69b2d8c8547b71ee9cfd4a5b653659da0041eba3f43965c0cc4941825a9f583 |
---|---|
cites | cdi_FETCH-LOGICAL-c2971-db69b2d8c8547b71ee9cfd4a5b653659da0041eba3f43965c0cc4941825a9f583 |
container_end_page | 9 |
container_issue | 1 |
container_start_page | 1 |
container_title | Journal of control theory and applications |
container_volume | 11 |
creator | Yin, Gang Wang, Le Yi Sun, Yu Casbeer, David Holsapple, Raymond Kingston, Derek |
description | This paper introduces a post-iteration averaging algorithm to achieve asymptotic optimality in convergence rates of stochastic approximation algorithms for consensus control with structural constraints. The algorithm involves two stages. The first stage is a coarse approximation obtained using a sequence of large stepsizes. Then, the second stage provides a refinement by averaging the iterates from the first stage. We show that the new algorithm is asymptotically efficient and gives the optimal convergence rates in the sense of the best scaling factor and 'smallest' possible asymptotic variance. |
doi_str_mv | 10.1007/s11768-013-2013-2 |
format | article |
fullrecord | <record><control><sourceid>wanfang_jour_proqu</sourceid><recordid>TN_cdi_wanfang_journals_kzllyyy_e201301001</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cqvip_id>44519257</cqvip_id><wanfj_id>kzllyyy_e201301001</wanfj_id><sourcerecordid>kzllyyy_e201301001</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2971-db69b2d8c8547b71ee9cfd4a5b653659da0041eba3f43965c0cc4941825a9f583</originalsourceid><addsrcrecordid>eNp9kE1LxDAQhosoqKs_wFu9iVDNZ9scRfwCwYueQ5qm3a7dpGZStf56s1vRm5eZEJ533pk3SU4wusAIFZeAcZGXGcI0I9uykxxgIWiGckJ34zsvSJZThvaTQ4AVQpyxojxI6iuY1kNwodOpG0K3Vn0XprRxPtXOgrEwQhamwaQQnF4q2IBqGLz7jGzonE1V3zrfheUa0hE626ZdMF4Fk6r32Nv4c5TsNaoHc_zTF8nL7c3z9X32-HT3cH31mGkiCpzVVS4qUpe65KyoCmyM0E3NFK9yTnMuaoUQw6ZStGFU5FwjrZlguCRciYaXdJGcz3M_lG2UbeXKjd5GR_n61ffTNEmzCQfFxHCEz2Y43vI2Gghy3YE2fa-scSPImBhmBDNGI4pnVHsH4E0jBx-v95PESG7il3P8Mo6VZFuihswaiKxtjf9b5j_R6Y_R0tn2Lep-nRjjWBBe0G-q65Vs</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1671421443</pqid></control><display><type>article</type><title>Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging</title><source>Springer Nature</source><creator>Yin, Gang ; Wang, Le Yi ; Sun, Yu ; Casbeer, David ; Holsapple, Raymond ; Kingston, Derek</creator><creatorcontrib>Yin, Gang ; Wang, Le Yi ; Sun, Yu ; Casbeer, David ; Holsapple, Raymond ; Kingston, Derek</creatorcontrib><description>This paper introduces a post-iteration averaging algorithm to achieve asymptotic optimality in convergence rates of stochastic approximation algorithms for consensus control with structural constraints. The algorithm involves two stages. The first stage is a coarse approximation obtained using a sequence of large stepsizes. Then, the second stage provides a refinement by averaging the iterates from the first stage. We show that the new algorithm is asymptotically efficient and gives the optimal convergence rates in the sense of the best scaling factor and 'smallest' possible asymptotic variance.</description><identifier>ISSN: 1672-6340</identifier><identifier>EISSN: 1993-0623</identifier><identifier>DOI: 10.1007/s11768-013-2013-2</identifier><language>eng</language><publisher>Heidelberg: South China University of Technology and Academy of Mathematics and Systems Science, CAS</publisher><subject>Algorithms ; Approximation ; Asymptotic properties ; Complexity ; Computational Intelligence ; Control ; Control and Systems Theory ; Control theory ; Convergence ; Engineering ; Mathematical analysis ; Mechatronics ; Optimization ; Robotics ; Stochasticity ; Systems Theory ; 平均 ; 最优收敛速度 ; 渐近方差 ; 渐近最优 ; 渐近有效 ; 近似算法 ; 迭代点 ; 随机逼近算法</subject><ispartof>Journal of control theory and applications, 2013-02, Vol.11 (1), p.1-9</ispartof><rights>South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2013</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2971-db69b2d8c8547b71ee9cfd4a5b653659da0041eba3f43965c0cc4941825a9f583</citedby><cites>FETCH-LOGICAL-c2971-db69b2d8c8547b71ee9cfd4a5b653659da0041eba3f43965c0cc4941825a9f583</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://image.cqvip.com/vip1000/qk/87672X/87672X.jpg</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Yin, Gang</creatorcontrib><creatorcontrib>Wang, Le Yi</creatorcontrib><creatorcontrib>Sun, Yu</creatorcontrib><creatorcontrib>Casbeer, David</creatorcontrib><creatorcontrib>Holsapple, Raymond</creatorcontrib><creatorcontrib>Kingston, Derek</creatorcontrib><title>Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging</title><title>Journal of control theory and applications</title><addtitle>J. Control Theory Appl</addtitle><addtitle>Journal of Control Theory and Applications</addtitle><description>This paper introduces a post-iteration averaging algorithm to achieve asymptotic optimality in convergence rates of stochastic approximation algorithms for consensus control with structural constraints. The algorithm involves two stages. The first stage is a coarse approximation obtained using a sequence of large stepsizes. Then, the second stage provides a refinement by averaging the iterates from the first stage. We show that the new algorithm is asymptotically efficient and gives the optimal convergence rates in the sense of the best scaling factor and 'smallest' possible asymptotic variance.</description><subject>Algorithms</subject><subject>Approximation</subject><subject>Asymptotic properties</subject><subject>Complexity</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Control and Systems Theory</subject><subject>Control theory</subject><subject>Convergence</subject><subject>Engineering</subject><subject>Mathematical analysis</subject><subject>Mechatronics</subject><subject>Optimization</subject><subject>Robotics</subject><subject>Stochasticity</subject><subject>Systems Theory</subject><subject>平均</subject><subject>最优收敛速度</subject><subject>渐近方差</subject><subject>渐近最优</subject><subject>渐近有效</subject><subject>近似算法</subject><subject>迭代点</subject><subject>随机逼近算法</subject><issn>1672-6340</issn><issn>1993-0623</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhosoqKs_wFu9iVDNZ9scRfwCwYueQ5qm3a7dpGZStf56s1vRm5eZEJ533pk3SU4wusAIFZeAcZGXGcI0I9uykxxgIWiGckJ34zsvSJZThvaTQ4AVQpyxojxI6iuY1kNwodOpG0K3Vn0XprRxPtXOgrEwQhamwaQQnF4q2IBqGLz7jGzonE1V3zrfheUa0hE626ZdMF4Fk6r32Nv4c5TsNaoHc_zTF8nL7c3z9X32-HT3cH31mGkiCpzVVS4qUpe65KyoCmyM0E3NFK9yTnMuaoUQw6ZStGFU5FwjrZlguCRciYaXdJGcz3M_lG2UbeXKjd5GR_n61ffTNEmzCQfFxHCEz2Y43vI2Gghy3YE2fa-scSPImBhmBDNGI4pnVHsH4E0jBx-v95PESG7il3P8Mo6VZFuihswaiKxtjf9b5j_R6Y_R0tn2Lep-nRjjWBBe0G-q65Vs</recordid><startdate>20130201</startdate><enddate>20130201</enddate><creator>Yin, Gang</creator><creator>Wang, Le Yi</creator><creator>Sun, Yu</creator><creator>Casbeer, David</creator><creator>Holsapple, Raymond</creator><creator>Kingston, Derek</creator><general>South China University of Technology and Academy of Mathematics and Systems Science, CAS</general><general>Department of Mathematics, Wayne State University, Detroit, MI 48202, U.S.A.%Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, U.S.A.%Quantitative Business Analysis Department, Siena College, Loudonville, New York 12211, U.S.A.%Control Science Center of Excellence, Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, U.S.A</general><scope>2RA</scope><scope>92L</scope><scope>CQIGP</scope><scope>W92</scope><scope>~WA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>L7M</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20130201</creationdate><title>Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging</title><author>Yin, Gang ; Wang, Le Yi ; Sun, Yu ; Casbeer, David ; Holsapple, Raymond ; Kingston, Derek</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2971-db69b2d8c8547b71ee9cfd4a5b653659da0041eba3f43965c0cc4941825a9f583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>Approximation</topic><topic>Asymptotic properties</topic><topic>Complexity</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Control and Systems Theory</topic><topic>Control theory</topic><topic>Convergence</topic><topic>Engineering</topic><topic>Mathematical analysis</topic><topic>Mechatronics</topic><topic>Optimization</topic><topic>Robotics</topic><topic>Stochasticity</topic><topic>Systems Theory</topic><topic>平均</topic><topic>最优收敛速度</topic><topic>渐近方差</topic><topic>渐近最优</topic><topic>渐近有效</topic><topic>近似算法</topic><topic>迭代点</topic><topic>随机逼近算法</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yin, Gang</creatorcontrib><creatorcontrib>Wang, Le Yi</creatorcontrib><creatorcontrib>Sun, Yu</creatorcontrib><creatorcontrib>Casbeer, David</creatorcontrib><creatorcontrib>Holsapple, Raymond</creatorcontrib><creatorcontrib>Kingston, Derek</creatorcontrib><collection>维普_期刊</collection><collection>中文科技期刊数据库-CALIS站点</collection><collection>维普中文期刊数据库</collection><collection>中文科技期刊数据库-工程技术</collection><collection>中文科技期刊数据库- 镜像站点</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Journal of control theory and applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yin, Gang</au><au>Wang, Le Yi</au><au>Sun, Yu</au><au>Casbeer, David</au><au>Holsapple, Raymond</au><au>Kingston, Derek</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging</atitle><jtitle>Journal of control theory and applications</jtitle><stitle>J. Control Theory Appl</stitle><addtitle>Journal of Control Theory and Applications</addtitle><date>2013-02-01</date><risdate>2013</risdate><volume>11</volume><issue>1</issue><spage>1</spage><epage>9</epage><pages>1-9</pages><issn>1672-6340</issn><eissn>1993-0623</eissn><abstract>This paper introduces a post-iteration averaging algorithm to achieve asymptotic optimality in convergence rates of stochastic approximation algorithms for consensus control with structural constraints. The algorithm involves two stages. The first stage is a coarse approximation obtained using a sequence of large stepsizes. Then, the second stage provides a refinement by averaging the iterates from the first stage. We show that the new algorithm is asymptotically efficient and gives the optimal convergence rates in the sense of the best scaling factor and 'smallest' possible asymptotic variance.</abstract><cop>Heidelberg</cop><pub>South China University of Technology and Academy of Mathematics and Systems Science, CAS</pub><doi>10.1007/s11768-013-2013-2</doi><tpages>9</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1672-6340 |
ispartof | Journal of control theory and applications, 2013-02, Vol.11 (1), p.1-9 |
issn | 1672-6340 1993-0623 |
language | eng |
recordid | cdi_wanfang_journals_kzllyyy_e201301001 |
source | Springer Nature |
subjects | Algorithms Approximation Asymptotic properties Complexity Computational Intelligence Control Control and Systems Theory Control theory Convergence Engineering Mathematical analysis Mechatronics Optimization Robotics Stochasticity Systems Theory 平均 最优收敛速度 渐近方差 渐近最优 渐近有效 近似算法 迭代点 随机逼近算法 |
title | Asymptotic optimality for consensus-type stochastic approximation algorithms using iterate averaging |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T09%3A07%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-wanfang_jour_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Asymptotic%20optimality%20for%20consensus-type%20stochastic%20approximation%20algorithms%20using%20iterate%20averaging&rft.jtitle=Journal%20of%20control%20theory%20and%20applications&rft.au=Yin,%20Gang&rft.date=2013-02-01&rft.volume=11&rft.issue=1&rft.spage=1&rft.epage=9&rft.pages=1-9&rft.issn=1672-6340&rft.eissn=1993-0623&rft_id=info:doi/10.1007/s11768-013-2013-2&rft_dat=%3Cwanfang_jour_proqu%3Ekzllyyy_e201301001%3C/wanfang_jour_proqu%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c2971-db69b2d8c8547b71ee9cfd4a5b653659da0041eba3f43965c0cc4941825a9f583%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1671421443&rft_id=info:pmid/&rft_cqvip_id=44519257&rft_wanfj_id=kzllyyy_e201301001&rfr_iscdi=true |