Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks

Jianghai Hu; Yingying Xiao; Ji Liu

doi:10.1109/CDC.2018.8619467

Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks

Jianghai Hu
, Yingying Xiao
, Ji Liu

Electrical Engineering

Purdue University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

In this paper, we study the optimization problems for a group of agents whose individual objective functions and constraints may depend on the variables of neighboring agents. Several algorithms are proposed based on operator splitting techniques that can iteratively converge to an optimal primal (or dual) solution of the optimization problems. Then, via random coordinate updates, asynchronous implementations of the algorithms are developed with low computation and communication complexity and guaranteed almost sure convergence to an optimal solution. Numerical results are presented to illustrate the proposed algorithms.

Original language	English
Title of host publication	2018 IEEE Conference on Decision and Control, CDC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2420-2425
Number of pages	6
ISBN (Electronic)	9781538613955
DOIs	https://doi.org/10.1109/CDC.2018.8619467
State	Published - Jul 2 2018
Event	57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: Dec 17 2018 → Dec 19 2018

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2018-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	57th IEEE Conference on Decision and Control, CDC 2018
Country/Territory	United States
City	Miami
Period	12/17/18 → 12/19/18

Access to Document

10.1109/CDC.2018.8619467

Cite this

Hu, J., Xiao, Y., & Liu, J. (2018). Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 2420-2425). Article 8619467 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619467

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title = "Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks",

abstract = "In this paper, we study the optimization problems for a group of agents whose individual objective functions and constraints may depend on the variables of neighboring agents. Several algorithms are proposed based on operator splitting techniques that can iteratively converge to an optimal primal (or dual) solution of the optimization problems. Then, via random coordinate updates, asynchronous implementations of the algorithms are developed with low computation and communication complexity and guaranteed almost sure convergence to an optimal solution. Numerical results are presented to illustrate the proposed algorithms.",

author = "Jianghai Hu and Yingying Xiao and Ji Liu",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 57th IEEE Conference on Decision and Control, CDC 2018 ; Conference date: 17-12-2018 Through 19-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/CDC.2018.8619467",

language = "English",

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Hu, J, Xiao, Y & Liu, J 2018, Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619467, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 2420-2425, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8619467

Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks. / Hu, Jianghai; Xiao, Yingying; Liu, Ji.
2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2420-2425 8619467 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Liu, Ji

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N2 - In this paper, we study the optimization problems for a group of agents whose individual objective functions and constraints may depend on the variables of neighboring agents. Several algorithms are proposed based on operator splitting techniques that can iteratively converge to an optimal primal (or dual) solution of the optimization problems. Then, via random coordinate updates, asynchronous implementations of the algorithms are developed with low computation and communication complexity and guaranteed almost sure convergence to an optimal solution. Numerical results are presented to illustrate the proposed algorithms.

AB - In this paper, we study the optimization problems for a group of agents whose individual objective functions and constraints may depend on the variables of neighboring agents. Several algorithms are proposed based on operator splitting techniques that can iteratively converge to an optimal primal (or dual) solution of the optimization problems. Then, via random coordinate updates, asynchronous implementations of the algorithms are developed with low computation and communication complexity and guaranteed almost sure convergence to an optimal solution. Numerical results are presented to illustrate the proposed algorithms.

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U2 - 10.1109/CDC.2018.8619467

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T3 - Proceedings of the IEEE Conference on Decision and Control

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Y2 - 17 December 2018 through 19 December 2018

ER -

Distributed Algorithms for Solving Locally Coupled Optimization Problems on Agent Networks

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