Accelerated Multi-Agent Optimization Method over Stochastic Networks

Wicak Ananduta; Carlos Ocampo-Martinez; Angelia Nedic

doi:10.1109/CDC42340.2020.9304307

Accelerated Multi-Agent Optimization Method over Stochastic Networks

Wicak Ananduta, Carlos Ocampo-Martinez, Angelia Nedic

Team Bart De Schutter

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

3 Citations (Scopus)

Abstract

We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of \mathcal{O}(1/{k^2}). Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.

Original language	English
Title of host publication	Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	2961-2966
ISBN (Electronic)	978-1-7281-7447-1
DOIs	https://doi.org/10.1109/CDC42340.2020.9304307
Publication status	Published - 2020
Event	59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020

Conference

Conference	59th IEEE Conference on Decision and Control, CDC 2020
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	14/12/20 → 18/12/20

Keywords

accelerated gradient method
distributed method
distributed optimal power flow problem
multi-agent optimization

Access to Document

10.1109/CDC42340.2020.9304307

Cite this

@inproceedings{29b2fca6b1784a8fa3f49f3ee3741e5a,

title = "Accelerated Multi-Agent Optimization Method over Stochastic Networks",

abstract = "We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of \mathcal{O}(1/{k^2}). Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.",

keywords = "accelerated gradient method, distributed method, distributed optimal power flow problem, multi-agent optimization",

author = "Wicak Ananduta and Carlos Ocampo-Martinez and Angelia Nedic",

year = "2020",

doi = "10.1109/CDC42340.2020.9304307",

language = "English",

pages = "2961--2966",

booktitle = "Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020",

publisher = "IEEE",

address = "United States",

note = "59th IEEE Conference on Decision and Control, CDC 2020 ; Conference date: 14-12-2020 Through 18-12-2020",

}

Ananduta, W, Ocampo-Martinez, C & Nedic, A 2020, Accelerated Multi-Agent Optimization Method over Stochastic Networks. in Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020. IEEE, Piscataway, NJ, USA, pp. 2961-2966, 59th IEEE Conference on Decision and Control, CDC 2020, Virtual, Jeju Island, Korea, Republic of, 14/12/20. https://doi.org/10.1109/CDC42340.2020.9304307

Accelerated Multi-Agent Optimization Method over Stochastic Networks. / Ananduta, Wicak; Ocampo-Martinez, Carlos; Nedic, Angelia.
Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020. Piscataway, NJ, USA: IEEE, 2020. p. 2961-2966.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Accelerated Multi-Agent Optimization Method over Stochastic Networks

AU - Ananduta, Wicak

AU - Ocampo-Martinez, Carlos

AU - Nedic, Angelia

PY - 2020

Y1 - 2020

N2 - We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of \mathcal{O}(1/{k^2}). Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.

AB - We propose a distributed method to solve a multi-agent optimization problem with strongly convex cost function and equality coupling constraints. The method is based on Nesterov's accelerated gradient approach and works over stochastically time-varying communication networks. We consider the standard assumptions of Nesterov's method and show that the sequence of the expected dual values converge toward the optimal value with the rate of \mathcal{O}(1/{k^2}). Furthermore, we provide a simulation study of solving an optimal power flow problem with a well-known benchmark case.

KW - accelerated gradient method

KW - distributed method

KW - distributed optimal power flow problem

KW - multi-agent optimization

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U2 - 10.1109/CDC42340.2020.9304307

DO - 10.1109/CDC42340.2020.9304307

M3 - Conference contribution

AN - SCOPUS:85099880453

SP - 2961

EP - 2966

BT - Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020

PB - IEEE

CY - Piscataway, NJ, USA

T2 - 59th IEEE Conference on Decision and Control, CDC 2020

Y2 - 14 December 2020 through 18 December 2020

ER -

Accelerated Multi-Agent Optimization Method over Stochastic Networks

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