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dc.contributor.authorLoizou, Nicolas
dc.contributor.authorRichtarik, Peter
dc.date.accessioned2021-09-16T06:06:42Z
dc.date.available2019-11-27T10:58:54Z
dc.date.available2021-09-16T06:06:42Z
dc.date.issued2021
dc.identifier.citationLoizou, N., & Richtarik, P. (2021). Revisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols. IEEE Transactions on Information Theory, 1–1. doi:10.1109/tit.2021.3113285
dc.identifier.issn1557-9654
dc.identifier.doi10.1109/TIT.2021.3113285
dc.identifier.urihttp://hdl.handle.net/10754/660273
dc.description.abstractIn this work we present a new framework for the analysis and design of randomized gossip algorithms for solving the average consensus problem. We show how classical randomized iterative methods for solving linear systems can be interpreted as gossip algorithms when applied to special systems encoding the underlying network and explain in detail their decentralized nature. Our general framework recovers a comprehensive array of well-known gossip algorithms as special cases, including the pairwise randomized gossip algorithm and path averaging gossip, and allows for the development of provably faster variants. The flexibility of the new approach enables the design of a number of new specific gossip methods. For instance, we propose and analyze novel block and the first provably accelerated randomized gossip protocols, and dual randomized gossip algorithms. From a numerical analysis viewpoint, our work is the first that explores in depth the decentralized nature of randomized iterative methods for linear systems and proposes them as methods for solving the average consensus problem. We evaluate the performance of the proposed gossip protocols by performing extensive experimental testing on typical wireless network topologies.
dc.description.sponsorshipThe authors would like to thank Mike Rabbat for useful discussions related to the literature of gossip algorithms and for his comments during the writing of this paper.
dc.publisherIEEE
dc.relation.urlhttps://ieeexplore.ieee.org/document/9539193/
dc.relation.urlhttps://ieeexplore.ieee.org/document/9539193/
dc.relation.urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9539193
dc.rights(c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectRandomized gossip algorithms
dc.subjectaverage consensus
dc.subjectweighted average consensus
dc.subjectstochastic methods
dc.subjectlinear systems
dc.subjectrandomized Kaczmarz
dc.subjectrandomized block Kaczmarz
dc.subjectrandomized coordinate descent
dc.subjectheavy ball momentum
dc.subjectNesterov’s acceleration
dc.subjectduality
dc.subjectconvex optimization
dc.subjectwireless sensor networks
dc.titleRevisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Information Theory
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer Science and Operations Research, University of Montreal, and Mila - Quebec Artificial Intelligence Institute, Canada.
dc.identifier.pages1-1
dc.identifier.arxivid1905.08645
kaust.personRichtarik, Peter
refterms.dateFOA2019-11-27T10:59:22Z


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