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Accelerated Gradient Methods for Networked Optimization
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2011 (English)In: 2011 AMERICAN CONTROL CONFERENCE, 2011Conference paper, Published paper (Refereed)
Abstract [en]

This paper explores the use of accelerated gradient methods in networked optimization. Optimal algorithm parameters and associated convergence rates are derived for distributed resource allocation and consensus problems, and the practical performance of the accelerated gradient algorithms are shown to outperform alternatives in the literature. Since the optimal parameters for the accelerated gradient method depends on upper and lower bounds of the Hessian, we study how errors in these estimates influence the convergence rate of the algorithm. This analysis identifies, among other things, cases where erroneous estimates of the Hessian bounds cause the accelerated method to have slower convergence than the corresponding (non-accelerated) gradient method. An application to Internet congestion control illustrates these issues.

Place, publisher, year, edition, pages
2011.
Series
Proceedings of the American Control Conference, ISSN 0743-1619
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-50337ISI: 000295376002047Scopus ID: 2-s2.0-80053136229ISBN: 978-1-4577-0081-1 (print)OAI: oai:DiVA.org:kth-50337DiVA: diva2:480291
Conference
American Control Conference (ACC) JUN 29-JUL 01, 2011 San Fransisco, CA
Note
QC 20120119Available from: 2012-01-19 Created: 2011-12-05 Last updated: 2012-02-17Bibliographically approved

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Scopushttp://www.s3.kth.se/~imansh/Publication_List_files/Acc011final_v2.pdf

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