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dc.contributor.authorChambolle, Antonin
dc.contributor.authorEhrhardt, Matthias J.
dc.contributor.authorRichtarik, Peter
dc.contributor.authorSchönlieb, Carola-Bibiane
dc.date.accessioned2017-12-28T07:32:16Z
dc.date.available2017-12-28T07:32:16Z
dc.date.issued2017-06-15
dc.identifier.urihttp://hdl.handle.net/10754/626553
dc.description.abstractWe propose a stochastic extension of the primal-dual hybrid gradient algorithm studied by Chambolle and Pock in 2011 to solve saddle point problems that are separable in the dual variable. The analysis is carried out for general convex-concave saddle point problems and problems that are either partially smooth / strongly convex or fully smooth / strongly convex. We perform the analysis for arbitrary samplings of dual variables, and obtain known deterministic results as a special case. Several variants of our stochastic method significantly outperform the deterministic variant on a variety of imaging tasks.
dc.publisherarXiv
dc.relation.urlhttp://arxiv.org/abs/1706.04957v1
dc.relation.urlhttp://arxiv.org/pdf/1706.04957v1
dc.rightsArchived with thanks to arXiv
dc.titleStochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Application
dc.typePreprint
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentVisual Computing Center (VCC)
dc.eprint.versionPre-print
dc.contributor.institutionCMAP, Ecole Polytechnique, CNRS, France
dc.contributor.institutionDepartment for Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK
dc.contributor.institutionThe Alan Turing Institute, London, United Kingdom
dc.contributor.institutionSchool of Mathematics, University of Edinburgh, Edinburgh, United Kingdom
dc.identifier.arxivid1706.04957
kaust.personRichtarik, Peter
refterms.dateFOA2018-06-14T09:25:06Z


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