One-Point Residual Feedback Algorithms for Distributed Online Convex and Non-convex Optimization

Fuente: arXiv
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Hauptverfasser: Wang, Yaowen, Mo, Lipo, Zuo, Min, Zheng, Yuanshi
Format: Preprint
Veröffentlicht: 2025
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author Wang, Yaowen
Mo, Lipo
Zuo, Min
Zheng, Yuanshi
author_facet Wang, Yaowen
Mo, Lipo
Zuo, Min
Zheng, Yuanshi
contents This paper mainly addresses the distributed online optimization problem where the local objective functions are assumed to be convex or non-convex. First, the distributed algorithms are proposed for the convex and non-convex situations, where the one-point residual feedback technology is introduced to estimate gradient of local objective functions. Then the regret bounds of the proposed algorithms are derived respectively under the assumption that the local objective functions are Lipschitz or smooth, which implies that the regrets are sublinear. Finally, we give two numerical examples of distributed convex optimization and distributed resources allocation problem to illustrate the effectiveness of the proposed algorithm.
format Preprint
id arxiv_https___arxiv_org_abs_2503_16845
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle One-Point Residual Feedback Algorithms for Distributed Online Convex and Non-convex Optimization
Wang, Yaowen
Mo, Lipo
Zuo, Min
Zheng, Yuanshi
Optimization and Control
Systems and Control
This paper mainly addresses the distributed online optimization problem where the local objective functions are assumed to be convex or non-convex. First, the distributed algorithms are proposed for the convex and non-convex situations, where the one-point residual feedback technology is introduced to estimate gradient of local objective functions. Then the regret bounds of the proposed algorithms are derived respectively under the assumption that the local objective functions are Lipschitz or smooth, which implies that the regrets are sublinear. Finally, we give two numerical examples of distributed convex optimization and distributed resources allocation problem to illustrate the effectiveness of the proposed algorithm.
title One-Point Residual Feedback Algorithms for Distributed Online Convex and Non-convex Optimization
topic Optimization and Control
Systems and Control
url https://arxiv.org/abs/2503.16845