A Stochastic Variance Reduction Algorithm with Bregman Distances for Structured Composite Problems
Optimization and Control
2021-03-17 v1
Abstract
We develop a novel stochastic primal dual splitting method with Bregman distances for solving a structured composite problems involving infimal convolutions in non-Euclidean spaces. The sublinear convergence in expectation of the primal-dual gap is proved under mild conditions on stepsize for the general case. The linear convergence rate is obtained under additional condition like the strong convexity relative to Bregman functions.
Cite
@article{arxiv.2103.08822,
title = {A Stochastic Variance Reduction Algorithm with Bregman Distances for Structured Composite Problems},
author = {Nguyen Van Dung and Băng Công Vũ},
journal= {arXiv preprint arXiv:2103.08822},
year = {2021}
}