A stochastic coordinate descent inertial primal-dual algorithm for large-scale composite optimization
Abstract
We consider an inertial primal-dual algorithm to compute the minimizations of the sum of two convex functions and the composition of another convex function with a continuous linear operator. With the idea of coordinate descent, we design a stochastic coordinate descent inertial primal-dual splitting algorithm. Moreover, in order to prove the convergence of the proposed inertial algorithm, we formulate first the inertial version of the randomized Krasnosel'skii-Mann iterations algorithm for approximating the set of fixed points of a nonexpansive operator and investigate its convergence properties. Then the convergence of stochastic coordinate descent inertial primal-dual splitting algorithm is derived by applying the inertial version of the randomized Krasnosel'skii-Mann iterations to the composition of the proximity operator.
Cite
@article{arxiv.1604.04845,
title = {A stochastic coordinate descent inertial primal-dual algorithm for large-scale composite optimization},
author = {Meng Wen and Yu-Chao Tang and Jigen Peng},
journal= {arXiv preprint arXiv:1604.04845},
year = {2016}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1604.04172, arXiv:1604.04282; substantial text overlap with arXiv:1407.0898 by other authors