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    Stochastic Primal-Dual Hybrid Gradient Algorithm with Arbitrary Sampling and Imaging Application

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    1706.04957v1.pdf
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    Description:
    Preprint
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    Type
    Preprint
    Authors
    Chambolle, Antonin
    Ehrhardt, Matthias J.
    Richtarik, Peter cc
    Schönlieb, Carola-Bibiane
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Extreme Computing Research Center
    Visual Computing Center (VCC)
    Date
    2017-06-15
    Permanent link to this record
    http://hdl.handle.net/10754/626553
    
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    Abstract
    We 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.
    Publisher
    arXiv
    arXiv
    1706.04957
    Additional Links
    http://arxiv.org/abs/1706.04957v1
    http://arxiv.org/pdf/1706.04957v1
    Collections
    Preprints; Extreme Computing Research Center; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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