Skip to Main content Skip to Navigation
Conference papers

Stochastic forward-backward and primal-dual approximation algorithms with application to online image restoration

Abstract : Stochastic approximation techniques have been used in various contexts in data science. We propose a stochastic version of the forward-backward algorithm for minimizing the sum of two convex functions, one of which is not necessarily smooth. Our framework can handle stochastic approximations of the gradient of the smooth function and allows for stochastic errors in the evaluation of the proximity operator of the nonsmooth function. The almost sure convergence of the iterates generated by the algorithm to a minimizer is established under relatively mild assumptions. We also propose a stochastic version of a popular primal-dual proximal splitting algorithm, establish its convergence, and apply it to an online image restoration problem.
Complete list of metadatas

Cited literature [39 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01422154
Contributor : Jean-Christophe Pesquet <>
Submitted on : Friday, December 23, 2016 - 8:59:55 PM
Last modification on : Wednesday, April 8, 2020 - 4:12:22 PM
Document(s) archivé(s) le : Friday, March 24, 2017 - 12:21:24 PM

File

eusipco2016.pdf
Files produced by the author(s)

Identifiers

Citation

Patrick Combettes, Jean-Christophe Pesquet. Stochastic forward-backward and primal-dual approximation algorithms with application to online image restoration. European Signal and Image Processing Conference (EUSIPCO 2016), Aug 2016, Budapest, Hungary. pp.1813 - 1817, ⟨10.1109/EUSIPCO.2016.7760561⟩. ⟨hal-01422154⟩

Share

Metrics

Record views

791

Files downloads

646