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dc.contributor.authorCondat, Laurent Pierre
dc.contributor.authorRichtarik, Peter
dc.date.accessioned2021-06-09T06:18:54Z
dc.date.available2021-06-09T06:18:54Z
dc.date.issued2021-06-06
dc.identifier.urihttp://hdl.handle.net/10754/669464
dc.description.abstractWe propose a generic variance-reduced algorithm, which we call MUltiple RANdomized Algorithm (MURANA), for minimizing a sum of several smooth functions plus a regularizer, in a sequential or distributed manner. Our method is formulated with general stochastic operators, which allow us to model various strategies for reducing the computational complexity. For example, MURANA supports sparse activation of the gradients, and also reduction of the communication load via compression of the update vectors. This versatility allows MURANA to cover many existing randomization mechanisms within a unified framework. However, MURANA also encodes new methods as special cases. We highlight one of them, which we call ELVIRA, and show that it improves upon Loopless SVRG.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2106.03056.pdf
dc.rightsArchived with thanks to arXiv
dc.titleMURANA: A Generic Framework for Stochastic Variance-Reduced Optimization
dc.typePreprint
dc.contributor.departmentVisual Computing Center (VCC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.eprint.versionPre-print
dc.identifier.arxivid2106.03056
kaust.personCondat, Laurent Pierre
kaust.personRichtarik, Peter
refterms.dateFOA2021-06-09T06:19:16Z


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