A Gradient Tracking Protocol for Optimization Over Nabla Fractional Multi-Agent Systems

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-03-17 DOI:10.1109/TSIPN.2024.3402354

Shuaiyu Zhou;Yiheng Wei;Shu Liang;Jinde Cao

引用次数: 0

Abstract

This paper investigates the distributed consensus optimization over a class of nabla fractional multi-agent systems (nFMASs). The proposed approach, built upon conventional gradient tracking techniques, addresses the specificity of the studied system by introducing a fractional gradient tracking protocol based on globally differential information of optimization variables. This protocol is applicable to nabla fractional systems of any order less than 1 and can be extended to integer discrete-time systems. The distributed optimization algorithms derived from this protocol ensure globally precise convergence under fixed step-sizes, thereby guaranteeing the feasibility of consensus optimization over nFMASs. Simulation results are presented to validate and substantiate the effectiveness of the proposed algorithms.

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纳布拉分数多代理系统优化的梯度跟踪协议

本文研究了一类 nabla 分数多代理系统（nFMAS）的分布式共识优化。所提出的方法以传统梯度跟踪技术为基础，通过引入基于优化变量全局差分信息的分数梯度跟踪协议，解决了所研究系统的特殊性问题。该协议适用于任何阶数小于 1 的 nabla 分数系统，并可扩展到整数离散时间系统。由该协议衍生出的分布式优化算法可确保在固定步长下的全局精确收敛，从而保证了在 nFMAS 上进行共识优化的可行性。仿真结果验证并证实了所提算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.