Frugal forward-backward splitting methods with deviations

Fuente: arXiv
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Autores principales: Fu, Yongyu, Zheng, Haowen, Dong, Qiao-Li, Qin, Xiaolong, Zhao, Jing
Formato: Preprint
Publicado: 2025
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author Fu, Yongyu
Zheng, Haowen
Dong, Qiao-Li
Qin, Xiaolong
Zhao, Jing
author_facet Fu, Yongyu
Zheng, Haowen
Dong, Qiao-Li
Qin, Xiaolong
Zhao, Jing
contents The deviation vectors provide additional degrees of freedom and effectively enhance the flexibility of algorithms. In the literature, the iterative schemes with deviations are constructed and their convergence analyses are performed on an inefficient, algorithm-by-algorithm basis. In this paper, we address these by providing a general framework of frugal forward-backward splitting methods with deviations for finding zeros in the sum of a finite number of maximally monotone operators and cocoercive operators. Our framework encompasses the Douglas--Rachford splitting method with deviations. A unified weak convergence analysis is made under mild conditions. Numerical experiments on Markowitz portfolio optimization problem are given to demonstrate the effectiveness of deviations.
format Preprint
id arxiv_https___arxiv_org_abs_2509_03865
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Frugal forward-backward splitting methods with deviations
Fu, Yongyu
Zheng, Haowen
Dong, Qiao-Li
Qin, Xiaolong
Zhao, Jing
Optimization and Control
The deviation vectors provide additional degrees of freedom and effectively enhance the flexibility of algorithms. In the literature, the iterative schemes with deviations are constructed and their convergence analyses are performed on an inefficient, algorithm-by-algorithm basis. In this paper, we address these by providing a general framework of frugal forward-backward splitting methods with deviations for finding zeros in the sum of a finite number of maximally monotone operators and cocoercive operators. Our framework encompasses the Douglas--Rachford splitting method with deviations. A unified weak convergence analysis is made under mild conditions. Numerical experiments on Markowitz portfolio optimization problem are given to demonstrate the effectiveness of deviations.
title Frugal forward-backward splitting methods with deviations
topic Optimization and Control
url https://arxiv.org/abs/2509.03865