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The ADMM algorithm for distributed averaging: convergence rates and optimal parameter selection
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-5491-4068
McGill University, Canada.
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Automatic Control.
2014 (English)In: 48th Asilomar Conference on Signals, Systems, and Computers 2014, IEEE conference proceedings, 2014, 783-787 p.Conference paper, Published paper (Refereed)
Abstract [en]

We derive the optimal step-size and overrelaxationparameter that minimizes the convergence time oftwo ADMM-based algorithms for distributed averaging. Ourstudy shows that the convergence times for given step-size andover-relaxation parameters depend on the spectral propertiesof the normalized Laplacian of the underlying communicationgraph. Motivated by this, we optimize the edge-weights of thecommunication graph to improve the convergence speed evenfurther. The performance of the ADMM algorithms with ourparameter selection are compared with alternatives from theliterature in extensive numerical simulations on random graphs.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014. 783-787 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-159704DOI: 10.1109/ACSSC.2014.7094556ISBN: 978-1-4799-8295-0 (print)OAI: oai:DiVA.org:kth-159704DiVA: diva2:787052
Conference
48th Asilomar Conference on Signals, Systems, and Computers, November 2-5 2014,Pacific Grove, CA, USA
Note

QC 20150511

Available from: 2015-02-09 Created: 2015-02-09 Last updated: 2015-05-11Bibliographically approved

Open Access in DiVA

preprint(593 kB)