Distributed optimization with hybrid linear constraints for multi-agent networks

被引:5
|
作者
Zheng, Yanling [1 ,3 ]
Liu, Qingshan [1 ,3 ]
Wang, Miao [2 ,3 ]
机构
[1] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China
[2] Southeast Univ, Sch Cyber Sci & Engn, Nanjing, Peoples R China
[3] Southeast Univ, Jiangsu Prov Key Lab Networked Collect Intelligen, Nanjing, Peoples R China
基金
中国国家自然科学基金;
关键词
convergence; distributed optimization; hybrid constraints; multi-agent networks; CONSENSUS;
D O I
10.1002/rnc.5927
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article investigates the distributed constrained optimization with hybrid linear constraints for multi-agent networks, in which all the agents collaboratively minimize the global objective function with a sum of convex local objective functions, while the constraints are more general with local and global restrictions on the agents. Based on matrix and graph theories, a discrete-time algorithm under distributed manner is designed to deal with the organized problems. In addition, the optimality of the presented algorithm is obtained under certain initial restriction for the agents. By virtue of a novel Lyapunov function and the optimal conditions, rigorous analysis shows the convergence of the multi-agent networks with undirected and connected graphs. Finally, two simulation examples are presented to validate the theoretical consequence.
引用
收藏
页码:2069 / 2083
页数:15
相关论文
共 50 条
  • [41] Distributed subgradient projection algorithm for multi-agent optimization with nonidentical constraints and switching topologies
    Institute of Astronautics and Aeronautics, University of Electronic Science and Technology of China, China
    不详
    Proc IEEE Conf Decis Control, (6813-6818):
  • [42] A novel method for distributed optimization with globally coupled constraints based on multi-agent systems
    Ge, Yiyang
    Mei, Xuehui
    Jiang, Haijun
    Qiu, Jianlong
    Yu, Zhiyong
    NEUROCOMPUTING, 2022, 487 : 289 - 299
  • [43] Distributed Subgradient Projection Algorithm for Multi-agent Optimization With Nonidentical Constraints and Switching Topologies
    Lin, Peng
    Ren, Wei
    2012 IEEE 51ST ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC), 2012, : 6813 - 6818
  • [44] Distributed partitioning algorithms for multi-agent networks with quadratic proximity metrics and sensing constraints
    Bakolas, E.
    SYSTEMS & CONTROL LETTERS, 2016, 91 : 36 - 42
  • [45] DISTRIBUTED STATE ESTIMATION IN MULTI-AGENT NETWORKS
    Das, Subhro
    Moura, Jose M. F.
    2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 4246 - 4250
  • [46] Distributed reinforcement learning in multi-agent networks
    Kar, Soummya
    Moura, Jose M. F.
    Poor, H. Vincent
    2013 IEEE 5TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL ADVANCES IN MULTI-SENSOR ADAPTIVE PROCESSING (CAMSAP 2013), 2013, : 296 - +
  • [47] Distributed Constrained Optimization Over Cloud-Based Multi-agent Networks
    Ling, Qing
    Xu, Wei
    Wang, Manxi
    Li, Yongcheng
    WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2016, 2016, 9798 : 91 - 102
  • [48] Distributed continuous-time optimization in multi-agent networks with undirected topology
    Fu, Zao
    Zhao, You
    Wen, Guanghui
    2019 IEEE 15TH INTERNATIONAL CONFERENCE ON CONTROL AND AUTOMATION (ICCA), 2019, : 1044 - 1049
  • [49] Distributed optimization consensus for multi-agent systems on matrix-weighted networks
    Miao, Suoxia
    Xiong, Ruxin
    An, Qing
    Bao, Cuihong
    Sun, Yaping
    Su, Housheng
    CHAOS, 2024, 34 (12)
  • [50] Gradient-Consensus Method for Distributed Optimization in Directed Multi-Agent Networks
    Khatana, Vivek
    Saraswat, Govind
    Patel, Sourav
    Salapaka, Murti, V
    2020 AMERICAN CONTROL CONFERENCE (ACC), 2020, : 4689 - 4694