A generalized algorithm for continuous-time distributed optimization

Hao Yu; Mani H. Dhullipalla; Tongwen Chen

doi:10.23919/ACC50511.2021.9483292

A generalized algorithm for continuous-time distributed optimization

Hao Yu, Mani H. Dhullipalla, Tongwen Chen

School of Automation

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

3 Citations (Scopus)

Abstract

This letter proposes a new generalized continuous-time distributed optimization algorithm which includes the popular modified-Lagrangian-based (MLB) algorithm and zero-gradient-sum (ZGS) algorithm as its special cases. The convergence of the proposed algorithm to the optimal point is analyzed in a uniform framework for directed and undirected communication topologies. Moreover, it is showed that by utilizing the Hessian of local cost functions, the design of algorithmic gains is made independent of global information even if the ZGS constraints are not satisfied. Finally, numerical simulations are provided to illustrate the feasibility of the theoretical results.

Original language	English
Title of host publication	2021 American Control Conference, ACC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	820-825
Number of pages	6
ISBN (Electronic)	9781665441971
DOIs	https://doi.org/10.23919/ACC50511.2021.9483292
Publication status	Published - 25 May 2021
Event	2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States Duration: 25 May 2021 → 28 May 2021

Publication series

Name	Proceedings of the American Control Conference
Volume	2021-May
ISSN (Print)	0743-1619

Conference

Conference	2021 American Control Conference, ACC 2021
Country/Territory	United States
City	Virtual, New Orleans
Period	25/05/21 → 28/05/21

Access to Document

10.23919/ACC50511.2021.9483292

Cite this

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title = "A generalized algorithm for continuous-time distributed optimization",

abstract = "This letter proposes a new generalized continuous-time distributed optimization algorithm which includes the popular modified-Lagrangian-based (MLB) algorithm and zero-gradient-sum (ZGS) algorithm as its special cases. The convergence of the proposed algorithm to the optimal point is analyzed in a uniform framework for directed and undirected communication topologies. Moreover, it is showed that by utilizing the Hessian of local cost functions, the design of algorithmic gains is made independent of global information even if the ZGS constraints are not satisfied. Finally, numerical simulations are provided to illustrate the feasibility of the theoretical results.",

author = "Hao Yu and Dhullipalla, {Mani H.} and Tongwen Chen",

note = "Publisher Copyright: {\textcopyright} 2021 American Automatic Control Council.; 2021 American Control Conference, ACC 2021 ; Conference date: 25-05-2021 Through 28-05-2021",

year = "2021",

month = may,

day = "25",

doi = "10.23919/ACC50511.2021.9483292",

language = "English",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2021 American Control Conference, ACC 2021",

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}

Yu, H, Dhullipalla, MH & Chen, T 2021, A generalized algorithm for continuous-time distributed optimization. in 2021 American Control Conference, ACC 2021., 9483292, Proceedings of the American Control Conference, vol. 2021-May, Institute of Electrical and Electronics Engineers Inc., pp. 820-825, 2021 American Control Conference, ACC 2021, Virtual, New Orleans, United States, 25/05/21. https://doi.org/10.23919/ACC50511.2021.9483292

A generalized algorithm for continuous-time distributed optimization. / Yu, Hao; Dhullipalla, Mani H.; Chen, Tongwen.
2021 American Control Conference, ACC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 820-825 9483292 (Proceedings of the American Control Conference; Vol. 2021-May).

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

TY - GEN

T1 - A generalized algorithm for continuous-time distributed optimization

AU - Yu, Hao

AU - Dhullipalla, Mani H.

AU - Chen, Tongwen

PY - 2021/5/25

Y1 - 2021/5/25

N2 - This letter proposes a new generalized continuous-time distributed optimization algorithm which includes the popular modified-Lagrangian-based (MLB) algorithm and zero-gradient-sum (ZGS) algorithm as its special cases. The convergence of the proposed algorithm to the optimal point is analyzed in a uniform framework for directed and undirected communication topologies. Moreover, it is showed that by utilizing the Hessian of local cost functions, the design of algorithmic gains is made independent of global information even if the ZGS constraints are not satisfied. Finally, numerical simulations are provided to illustrate the feasibility of the theoretical results.

AB - This letter proposes a new generalized continuous-time distributed optimization algorithm which includes the popular modified-Lagrangian-based (MLB) algorithm and zero-gradient-sum (ZGS) algorithm as its special cases. The convergence of the proposed algorithm to the optimal point is analyzed in a uniform framework for directed and undirected communication topologies. Moreover, it is showed that by utilizing the Hessian of local cost functions, the design of algorithmic gains is made independent of global information even if the ZGS constraints are not satisfied. Finally, numerical simulations are provided to illustrate the feasibility of the theoretical results.

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DO - 10.23919/ACC50511.2021.9483292

M3 - Conference contribution

AN - SCOPUS:85111940034

T3 - Proceedings of the American Control Conference

SP - 820

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BT - 2021 American Control Conference, ACC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 American Control Conference, ACC 2021

Y2 - 25 May 2021 through 28 May 2021

ER -

A generalized algorithm for continuous-time distributed optimization

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