Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework

Xing He; Junzhi Yu; Tingwen Huang; Chuandong Li; Chaojie Li

doi:10.1109/TCYB.2018.2869249

Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework

Xing He
, Junzhi Yu
, Tingwen Huang^*
, Chuandong Li
, Chaojie Li

^*Corresponding author for this work

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

37 Citations (Scopus)

Abstract

This paper presents the impulsive average quasi-consensus algorithm for distributed constrained convex optimization. First, the constrained optimization problem can be transformed into an unconstrained problem using the interior point method, and then a distributed algorithm is modeled by means of impulsive differential equation. In the framework of the continuous-time gradient method and algebraic graph theory, each agent can deal with one local objective function with local constraints. At the impulsive instants, each agent can communicate with its neighboring agents over the network. Under certain conditions, the impulsive average quasi-consensus is achieved. It is shown that the state of average quasi-consensus is the optimal solution of the aforementioned unconstrained optimization problem, and the state of each agent can also reach the neighborhood of the optimal solution. Finally, two numerical examples show the effectiveness of the proposed impulsive average quasi-consensus algorithm. Moreover, the feasibility of the approach is verified by an application to one sensor network localization problem. © 2018 IEEE.

Original language	English
Article number	8474351
Pages (from-to)	351-360
Number of pages	10
Journal	IEEE Transactions on Cybernetics
Volume	50
Issue number	1
Online published	27 Sept 2018
DOIs	https://doi.org/10.1109/TCYB.2018.2869249
Publication status	Published - Jan 2020
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 China Post-Doctoral Science Foundation under Grant 2016M600144 and Grant 2018T110154, in part by the Natural Science Foundation Project of Chongqing CSTC under Grant cstc2018jcyjAX0583, in part by the Research Foundation of Key Laboratory of Machine Perception and Children’s Intelligence Development funded by Chongqing University of Education, China, under Grant 16xjpt07, and in part by NPRP from the Qatar National Research Fund (a member of Qatar Foundation) under Grant 9-166-1-031.

Research Keywords

Distributed optimization
impulsive average quasi-consensus algorithm
impulsive communication framework
multiagent networks

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title = "Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework",

abstract = "This paper presents the impulsive average quasi-consensus algorithm for distributed constrained convex optimization. First, the constrained optimization problem can be transformed into an unconstrained problem using the interior point method, and then a distributed algorithm is modeled by means of impulsive differential equation. In the framework of the continuous-time gradient method and algebraic graph theory, each agent can deal with one local objective function with local constraints. At the impulsive instants, each agent can communicate with its neighboring agents over the network. Under certain conditions, the impulsive average quasi-consensus is achieved. It is shown that the state of average quasi-consensus is the optimal solution of the aforementioned unconstrained optimization problem, and the state of each agent can also reach the neighborhood of the optimal solution. Finally, two numerical examples show the effectiveness of the proposed impulsive average quasi-consensus algorithm. Moreover, the feasibility of the approach is verified by an application to one sensor network localization problem. {\textcopyright} 2018 IEEE.",

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AU - He, Xing

AU - Yu, Junzhi

AU - Huang, Tingwen

AU - Li, Chuandong

AU - Li, Chaojie

PY - 2020/1

Y1 - 2020/1

N2 - This paper presents the impulsive average quasi-consensus algorithm for distributed constrained convex optimization. First, the constrained optimization problem can be transformed into an unconstrained problem using the interior point method, and then a distributed algorithm is modeled by means of impulsive differential equation. In the framework of the continuous-time gradient method and algebraic graph theory, each agent can deal with one local objective function with local constraints. At the impulsive instants, each agent can communicate with its neighboring agents over the network. Under certain conditions, the impulsive average quasi-consensus is achieved. It is shown that the state of average quasi-consensus is the optimal solution of the aforementioned unconstrained optimization problem, and the state of each agent can also reach the neighborhood of the optimal solution. Finally, two numerical examples show the effectiveness of the proposed impulsive average quasi-consensus algorithm. Moreover, the feasibility of the approach is verified by an application to one sensor network localization problem. © 2018 IEEE.

AB - This paper presents the impulsive average quasi-consensus algorithm for distributed constrained convex optimization. First, the constrained optimization problem can be transformed into an unconstrained problem using the interior point method, and then a distributed algorithm is modeled by means of impulsive differential equation. In the framework of the continuous-time gradient method and algebraic graph theory, each agent can deal with one local objective function with local constraints. At the impulsive instants, each agent can communicate with its neighboring agents over the network. Under certain conditions, the impulsive average quasi-consensus is achieved. It is shown that the state of average quasi-consensus is the optimal solution of the aforementioned unconstrained optimization problem, and the state of each agent can also reach the neighborhood of the optimal solution. Finally, two numerical examples show the effectiveness of the proposed impulsive average quasi-consensus algorithm. Moreover, the feasibility of the approach is verified by an application to one sensor network localization problem. © 2018 IEEE.

KW - Distributed optimization

KW - impulsive average quasi-consensus algorithm

KW - impulsive communication framework

KW - multiagent networks

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JF - IEEE Transactions on Cybernetics

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ER -

Average Quasi-Consensus Algorithm for Distributed Constrained Optimization: Impulsive Communication Framework

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