A stochastic coordinate descent splitting primal-dual fixed point algorithm and applications to large-scale composite optimization
Optimization and Control
2016-04-18 v1
Abstract
We consider the problem of finding the minimizations of the sum of two convex functions and the composition of another convex function with a continuous linear operator from the view of fixed point algorithms based on proximity operators, which is is inspired by recent results of Chen, Huang and Zhang. With the idea of coordinate descent, we design a stochastic coordinate descent splitting primal- dual fixed point algorithm. Based on randomized krasnosel'skii mann iterations and the firmly nonexpansive properties of the proximity operator, we achieve the convergence of the proposed algorithms.
Cite
@article{arxiv.1604.04282,
title = {A stochastic coordinate descent splitting primal-dual fixed point algorithm and applications to large-scale composite optimization},
author = {Meng Wen and Yu-Chao Tang and Jigen Peng},
journal= {arXiv preprint arXiv:1604.04282},
year = {2016}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1407.0898 by other authors; substantial text overlap with arXiv:1604.04172