Stochastic forward-backward-half forward splitting algorithm with variance reduction

Fuente: arXiv
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Auteurs principaux: Qin, Liqian, Zhang, Yaxuan, Dong, Qiao-Li, Rassias, Michael Th.
Format: Preprint
Publié: 2023
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author Qin, Liqian
Zhang, Yaxuan
Dong, Qiao-Li
Rassias, Michael Th.
author_facet Qin, Liqian
Zhang, Yaxuan
Dong, Qiao-Li
Rassias, Michael Th.
contents In this paper, we present a stochastic forward-backward-half forward splitting algorithm with variance reduction for solving the structured monotone inclusion problem composed of a maximally monotone operator, a maximally monotone operator and a cocoercive operator in a separable real Hilbert space. By deffining a Lyapunov function, we establish the weak almost sure convergence of the proposed algorithm, and obtain the linear convergence when one of the maximally monotone operators is strongly monotone. Numerical examples are provided to show the performance of the proposed algorithm.
format Preprint
id arxiv_https___arxiv_org_abs_2312_00272
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Stochastic forward-backward-half forward splitting algorithm with variance reduction
Qin, Liqian
Zhang, Yaxuan
Dong, Qiao-Li
Rassias, Michael Th.
Optimization and Control
In this paper, we present a stochastic forward-backward-half forward splitting algorithm with variance reduction for solving the structured monotone inclusion problem composed of a maximally monotone operator, a maximally monotone operator and a cocoercive operator in a separable real Hilbert space. By deffining a Lyapunov function, we establish the weak almost sure convergence of the proposed algorithm, and obtain the linear convergence when one of the maximally monotone operators is strongly monotone. Numerical examples are provided to show the performance of the proposed algorithm.
title Stochastic forward-backward-half forward splitting algorithm with variance reduction
topic Optimization and Control
url https://arxiv.org/abs/2312.00272