A Constant Step Stochastic Douglas-Rachford Algorithm with Application to Non Separable Regularizations
Optimization and Control
2018-04-04 v1 Machine Learning
Abstract
The Douglas Rachford algorithm is an algorithm that converges to a minimizer of a sum of two convex functions. The algorithm consists in fixed point iterations involving computations of the proximity operators of the two functions separately. The paper investigates a stochastic version of the algorithm where both functions are random and the step size is constant. We establish that the iterates of the algorithm stay close to the set of solution with high probability when the step size is small enough. Application to structured regularization is considered.
Cite
@article{arxiv.1804.00934,
title = {A Constant Step Stochastic Douglas-Rachford Algorithm with Application to Non Separable Regularizations},
author = {Adil Salim and Pascal Bianchi and Walid Hachem},
journal= {arXiv preprint arXiv:1804.00934},
year = {2018}
}