A parallel block-coordinate approach for primal-dual splitting with arbitrary random block selection

Audrey Repetti, Emilie Chouzenoux, Jean-Christophe Pesquet

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


The solution of many applied problems relies on finding the minimizer of a sum of smooth and/or nonsmooth convex functions possibly involving linear operators. In the last years, primal-dual methods have shown their efficiency to solve such minimization problems, their main advantage being their ability to deal with linear operators with no need to invert them. However, when the problem size becomes increasingly large, the implementation of these algorithms can be complicated, due to memory limitation issues. A simple way to overcome this difficulty consists of splitting the original numerous variables into blocks of smaller dimension, corresponding to the available memory, and to process them separately. In this paper we propose a random block-coordinate primal-dual algorithm, converging almost surely to a solution to the considered minimization problem. Moreover, an application to large-size 3D mesh denoising is provided to show the numerical efficiency of our method.
Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference (EUSIPCO)
Number of pages5
ISBN (Electronic)9780992862633
Publication statusPublished - 28 Dec 2015
Event23rd European Signal Processing Conference 2015 - Nice, France
Duration: 31 Aug 20154 Sept 2015


Conference23rd European Signal Processing Conference 2015
Abbreviated titleEUSIPCO 2015


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