A randomized intertial primal-dual fixed point algorithm for monotone inclusions
Optimization and Control
2016-11-17 v1
Abstract
In this paper, we propose a randomized intertial block-coordinate primaldual fixed point algorithm to solve a wide array of monotone inclusion problems base on the modification of the heavy ball method of Nesterov. These methods rely on a sweep of blocks of variables which are activated at each iteration according to a random rule. To this end we formulate the inertial version of the Krasnosel'skii-Mann algorithm for approximating the set of fixed points of a quasinonexpansive operator, for which we also provide an exhaustive convergence analysis. As a by-product, we can obtain some intertial block-coordinate operator splitting methods for solving composite monotone inclusion and convex minimization problems.
Cite
@article{arxiv.1611.05142,
title = {A randomized intertial primal-dual fixed point algorithm for monotone inclusions},
author = {Meng Wen and Shigang Yue and Yuchao Tan and Jigen Peng},
journal= {arXiv preprint arXiv:1611.05142},
year = {2016}
}