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

被引：4

作者：

Zhou, Shuaiyu ^{[1
]}

Wei, Yiheng ^{[1
]}

Liang, Shu ^{[2
,3
]}

Cao, Jinde ^{[1
]}

机构：

[1] Southeast Univ, Sch Math, Nanjing 211189, Peoples R China

[2] Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China

[3] Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 201210, Peoples R China

来源：

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS | 2024年 / 10卷

基金：

中国国家自然科学基金;

关键词：

Optimization; Convergence; Linear programming; Protocols; Information processing; Heuristic algorithms; Multi-agent systems; Gradient tracking; nabla fractional system; distributed optimization; multi-agent network; DISTRIBUTED OPTIMIZATION; CONVEX-OPTIMIZATION; LINEAR CONVERGENCE; CONSENSUS; ALGORITHM;

D O I：

10.1109/TSIPN.2024.3402354

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：500 / 512

页数：13

共 73 条

[11]

Di Lorenzo P, 2016, INT CONF ACOUST SPEE, P4124, DOI 10.1109/ICASSP.2016.7472453

[12]

Di Lorenzo P, 2015, 2015 IEEE 6TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP), P229, DOI 10.1109/CAMSAP.2015.7383778

[13] Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender Systems [J].

Elahi, Fatemeh ;

Fazlali, Mahmood ;

Malazi, Hadi Tabatabaee ;

Elahi, Mehdi .

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2024, 35 (03) :470-483

[14] A divide-and-conquer algorithm for distributed optimization on networks [J].

Emirov, Nazar ;

Song, Guohui ;

Sun, Qiyu .

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2024, 70

[15] Distributed Nash Equilibrium Computation Under Round-Robin Scheduling Protocol [J].

Feng, Zhangcheng ;

Xu, Wenying ;

Cao, Jinde .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2024, 69 (01) :339-346

[16]

Goodrich C., 2015, DISCRETE FRACTIONAL

[17]

Hong X. L., 2023, P 6 INT S AUT SYST 2, P1, DOI [10.1109/ISAS59543.2023.10164582, DOI 10.1109/ISAS59543.2023.10164582]

[18] Distributed Optimization Algorithm for Multi-Robot Formation with Virtual Reference Center [J].

Huang, Jingyi ;

Zhou, Shuaiyu ;

Tu, Hua ;

Yao, Yuhong ;

Liu, Qingshan .

IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2022, 9 (04) :732-734

[19] Linear Convergence of Consensus-Based Quantized Optimization for Smooth and Strongly Convex Cost Functions [J].

Kajiyama, Yuichi ;

Hayashi, Naoki ;

Takai, Shigemasa .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2021, 66 (03) :1254-1261

[20] Tutorial on Dynamic Average Consensus THE PROBLEM, ITS APPLICATIONS, AND THE ALGORITHMS [J].

Kia, Solmaz S. ;

Van Scoy, Bryan ;

Cortes, Jorge ;

Freeman, Randy A. ;

Lynch, Kevin M. ;

Martinez, Sonia .

IEEE CONTROL SYSTEMS MAGAZINE, 2019, 39 (03) :40-72

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