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

Electrical Engineering

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

4 Scopus citations

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 (US)
Title of host publication	2020 59th IEEE Conference on Decision and Control, CDC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2961-2966
Number of pages	6
ISBN (Electronic)	9781728174471
DOIs	https://doi.org/10.1109/CDC42340.2020.9304307
State	Published - Dec 14 2020
Event	59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of Duration: Dec 14 2020 → Dec 18 2020

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2020-December

Conference

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

Keywords

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

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC42340.2020.9304307

Cite this

Ananduta, W., Ocampo-Martinez, C., & Nedic, A. (2020). Accelerated Multi-Agent Optimization Method over Stochastic Networks. In 2020 59th IEEE Conference on Decision and Control, CDC 2020 (pp. 2961-2966). Article 9304307 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC42340.2020.9304307

Accelerated Multi-Agent Optimization Method over Stochastic Networks. / Ananduta, Wicak; Ocampo-Martinez, Carlos; Nedic, Angelia.
2020 59th IEEE Conference on Decision and Control, CDC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2961-2966 9304307 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December).

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

Ananduta, W, Ocampo-Martinez, C & Nedic, A 2020, Accelerated Multi-Agent Optimization Method over Stochastic Networks. in 2020 59th IEEE Conference on Decision and Control, CDC 2020., 9304307, Proceedings of the IEEE Conference on Decision and Control, vol. 2020-December, Institute of Electrical and Electronics Engineers Inc., pp. 2961-2966, 59th IEEE Conference on Decision and Control, CDC 2020, Virtual, Jeju Island, Korea, Republic of, 12/14/20. https://doi.org/10.1109/CDC42340.2020.9304307

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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.",

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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.

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

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Accelerated Multi-Agent Optimization Method over Stochastic Networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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