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dc.contributor.authorCondat, Laurent
dc.contributor.authorMalinovsky, Grigory
dc.contributor.authorRichtarik, Peter
dc.date.accessioned2020-11-18T13:27:15Z
dc.date.available2020-11-18T13:27:15Z
dc.date.issued2020-10-02
dc.identifier.urihttp://hdl.handle.net/10754/666024
dc.description.abstractWe analyze several generic proximal splitting algorithms well suited for large-scale convex nonsmooth optimization. We derive sublinear and linear convergence results with new rates on the function value suboptimality or distance to the solution, as well as new accelerated versions, using varying stepsizes. In addition, we propose distributed variants of these algorithms, which can be accelerated as well. While most existing results are ergodic, our nonergodic results significantly broaden our understanding of primal-dual optimization algorithms.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2010.00952
dc.rightsArchived with thanks to arXiv
dc.titleDistributed Proximal Splitting Algorithms with Rates and Acceleration
dc.typePreprint
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.eprint.versionPre-print
dc.contributor.institutionMoscow Institute of Physics and Technology, Dolgoprudny, Russia.
dc.identifier.arxivid2010.00952
kaust.personCondat, Laurent
kaust.personRichtarik, Peter
refterms.dateFOA2020-11-18T13:27:53Z


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