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dc.contributor.authorLoizou, Nicolas
dc.contributor.authorRichtarik, Peter
dc.date.accessioned2019-11-27T10:58:54Z
dc.date.available2019-11-27T10:58:54Z
dc.date.issued2019-05-20
dc.identifier.urihttp://hdl.handle.net/10754/660273.1
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.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1905.08645
dc.rightsArchived with thanks to arXiv
dc.titleRevisiting Randomized Gossip Algorithms: General Framework, Convergence Rates and Novel Block and Accelerated Protocols
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.eprint.versionPre-print
dc.contributor.institutionUniversity of Edinburgh
dc.contributor.institutionMIPT
dc.identifier.arxivid1905.08645
kaust.personRichtarik, Peter
refterms.dateFOA2019-11-27T10:59:22Z


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