A Continuous-Time Algorithm for Distributed Optimization Based on Multiagent Networks

Xing He; Tingwen Huang; Junzhi Yu; Chaojie Li; Yushu Zhang

doi:10.1109/TSMC.2017.2780194

A Continuous-Time Algorithm for Distributed Optimization Based on Multiagent Networks

Xing He, Tingwen Huang^*, Junzhi Yu, Chaojie Li, Yushu Zhang

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

61 Citations (Scopus)

Abstract

Based on the multiagent networks, this paper introduces a continuous-time algorithm to deal with distributed convex optimization. Using nonsmooth analysis and algebraic graph theory, the distributed network algorithm is modeled by the aid of a nonautonomous differential inclusion, and each agent exchanges information from the first-order and the second-order neighbors. For any initial point, the solution of the proposed network can reach consensus to the set of minimizers if the graph has a spanning tree. In contrast to the existing continuous-time algorithms for distributed optimization, the proposed model holds the least number of state variables and relaxes the strongly connected weighted-balanced topology to the weaker case. The modified form of the proposed continuous-time algorithm is also given, and it is proven that this algorithm is suitable for solving distributed problems if the undirected network is connected. Finally, two numerical examples and an optimal placement problem confirm the effectiveness of the proposed continuous-time algorithm. © 2017 IEEE.

Original language	English
Pages (from-to)	2700-2709
Journal	IEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume	49
Issue number	12
Online published	25 Dec 2017
DOIs	https://doi.org/10.1109/TSMC.2017.2780194
Publication status	Published - Dec 2019
Externally published	Yes

Funding

This work was supported in part by the Natural Science Foundation of China under Grant 61773320, Grant 61633011, Grant 61725305, and Grant 61633020, in part by the Fundamental Research Funds for the Central Universities under Grant XDJK2016B017, in part by the China PostDoctoral Science Foundation under Grant 2016M600144, in part by the Research Foundation of Key Laboratory of Machine Perception and Children’s Intelligence Development funded by the Chongqing University of Education (16xjpt07), China, and in part by the Qatar National Research Fund (a member of Qatar Foundation) NPRP under Grant 9-166-1-031.

Research Keywords

Continuous-time algorithm
distributed optimization
multiagent network

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title = "A Continuous-Time Algorithm for Distributed Optimization Based on Multiagent Networks",

abstract = "Based on the multiagent networks, this paper introduces a continuous-time algorithm to deal with distributed convex optimization. Using nonsmooth analysis and algebraic graph theory, the distributed network algorithm is modeled by the aid of a nonautonomous differential inclusion, and each agent exchanges information from the first-order and the second-order neighbors. For any initial point, the solution of the proposed network can reach consensus to the set of minimizers if the graph has a spanning tree. In contrast to the existing continuous-time algorithms for distributed optimization, the proposed model holds the least number of state variables and relaxes the strongly connected weighted-balanced topology to the weaker case. The modified form of the proposed continuous-time algorithm is also given, and it is proven that this algorithm is suitable for solving distributed problems if the undirected network is connected. Finally, two numerical examples and an optimal placement problem confirm the effectiveness of the proposed continuous-time algorithm. {\textcopyright} 2017 IEEE.",

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AU - He, Xing

AU - Huang, Tingwen

AU - Yu, Junzhi

AU - Li, Chaojie

AU - Zhang, Yushu

PY - 2019/12

Y1 - 2019/12

N2 - Based on the multiagent networks, this paper introduces a continuous-time algorithm to deal with distributed convex optimization. Using nonsmooth analysis and algebraic graph theory, the distributed network algorithm is modeled by the aid of a nonautonomous differential inclusion, and each agent exchanges information from the first-order and the second-order neighbors. For any initial point, the solution of the proposed network can reach consensus to the set of minimizers if the graph has a spanning tree. In contrast to the existing continuous-time algorithms for distributed optimization, the proposed model holds the least number of state variables and relaxes the strongly connected weighted-balanced topology to the weaker case. The modified form of the proposed continuous-time algorithm is also given, and it is proven that this algorithm is suitable for solving distributed problems if the undirected network is connected. Finally, two numerical examples and an optimal placement problem confirm the effectiveness of the proposed continuous-time algorithm. © 2017 IEEE.

AB - Based on the multiagent networks, this paper introduces a continuous-time algorithm to deal with distributed convex optimization. Using nonsmooth analysis and algebraic graph theory, the distributed network algorithm is modeled by the aid of a nonautonomous differential inclusion, and each agent exchanges information from the first-order and the second-order neighbors. For any initial point, the solution of the proposed network can reach consensus to the set of minimizers if the graph has a spanning tree. In contrast to the existing continuous-time algorithms for distributed optimization, the proposed model holds the least number of state variables and relaxes the strongly connected weighted-balanced topology to the weaker case. The modified form of the proposed continuous-time algorithm is also given, and it is proven that this algorithm is suitable for solving distributed problems if the undirected network is connected. Finally, two numerical examples and an optimal placement problem confirm the effectiveness of the proposed continuous-time algorithm. © 2017 IEEE.

KW - Continuous-time algorithm

KW - distributed optimization

KW - multiagent network

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JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems

JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems

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A Continuous-Time Algorithm for Distributed Optimization Based on Multiagent Networks

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Research Keywords

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