Please use this identifier to cite or link to this item:
Title: Distributed aggregative optimization over multi-agent networks
Authors: Li, Xiuxian
Xie, Lihua
Hong, Yiguang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Li, X., Xie, L. & Hong, Y. (2021). Distributed aggregative optimization over multi-agent networks. IEEE Transactions On Automatic Control, 67(6), 3165-3171.
Project: RG72/19
Journal: IEEE Transactions on Automatic Control
Abstract: This article proposes a new framework for distributed optimization, called distributed aggregative optimization, which allows local objective functions to be dependent not only on their own decision variables, but also on the sum of functions of decision variables of all the agents. To handle this problem, a distributed algorithm, called distributed aggregative gradient tracking, is proposed and analyzed, where the global objective function is strongly convex, and the communication graph is balanced and strongly connected. It is shown that the algorithm can converge to the optimal variable at a linear rate. A numerical example is provided to corroborate the theoretical result.
ISSN: 0018-9286
DOI: 10.1109/TAC.2021.3095456
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Citations 20

Updated on Sep 22, 2023

Web of ScienceTM
Citations 20

Updated on Sep 21, 2023

Page view(s)

Updated on Sep 27, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.