Paper Info
Title
A Random Block-Coordinate Primal-Dual Proximal Algorithm With Application To 3d Mesh Denoising
Abstract
Primal-dual proximal optimization methods have recently gained much interest for dealing with very large-scale data sets encoutered in many application fields such as machine learning, computer vision and inverse problems [1-3]. In this work, we propose a novel random block-coordinate version of such algorithms allowing us to solve a wide array of convex variational problems. One of the main advantages of the proposed algorithm is its ability to solve composite problems involving large-size matrices without requiring any inversion. In addition, the almost sure convergence to an optimal solution to the problem is guaranteed. We illustrate the good performance of our method on a mesh denoising application.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP)
convex optimization, nonsmooth optimization, primal-dual algorithm, stochastic algorithm, block-coordinate algorithm, proximity operator, mesh processing, denoising, inverse problems
Field
DocType
ISSN
Convergence (routing),Convergence of random variables,Mathematical optimization,Stochastic optimization,Polygon mesh,Basis pursuit denoising,Computer science,Algorithm,Proximal Gradient Methods,Convex function,Inverse problem
Conference
1520-6149
Citations 
PageRank 
References 
3
0.39
22
Authors
3
Name
Order
Citations
PageRank
Audrey Repetti1766.84
Emilie Chouzenoux220226.37
Jean-Christophe Pesquet356046.10