An approximate dual subgradient algorithm for multi-agent non-convex optimization

Minghui Zhu; Sonia Martínez

doi:10.1109/CDC.2010.5717220

An approximate dual subgradient algorithm for multi-agent non-convex optimization

Minghui Zhu, Sonia Martínez

Electrical Engineering

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

10 Scopus citations

Abstract

We consider a multi-agent optimization problem where agents aim to cooperatively minimize a sum of local objective functions subject to a global inequality constraint and a global state constraint set. In contrast to existing papers, we do not require the objective, constraint functions, and state constraint sets to be convex. We propose a distributed approximate dual subgradient algorithm to enable agents to asymptotically converge to a pair of approximate primaldual solutions over dynamically changing network topologies. Convergence can be guaranteed provided that the Slater's condition and strong duality property are satisfied.

Original language	English (US)
Title of host publication	2010 49th IEEE Conference on Decision and Control, CDC 2010
Pages	7487-7492
Number of pages	6
DOIs	https://doi.org/10.1109/CDC.2010.5717220
State	Published - Dec 1 2010
Event	2010 49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, GA, United States Duration: Dec 15 2010 → Dec 17 2010

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0191-2216

Other

Other	2010 49th IEEE Conference on Decision and Control, CDC 2010
Country	United States
City	Atlanta, GA
Period	12/15/10 → 12/17/10

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC.2010.5717220

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Cite this

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abstract = "We consider a multi-agent optimization problem where agents aim to cooperatively minimize a sum of local objective functions subject to a global inequality constraint and a global state constraint set. In contrast to existing papers, we do not require the objective, constraint functions, and state constraint sets to be convex. We propose a distributed approximate dual subgradient algorithm to enable agents to asymptotically converge to a pair of approximate primaldual solutions over dynamically changing network topologies. Convergence can be guaranteed provided that the Slater's condition and strong duality property are satisfied.",

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Zhu, M & Martínez, S 2010, An approximate dual subgradient algorithm for multi-agent non-convex optimization. in 2010 49th IEEE Conference on Decision and Control, CDC 2010., 5717220, Proceedings of the IEEE Conference on Decision and Control, pp. 7487-7492, 2010 49th IEEE Conference on Decision and Control, CDC 2010, Atlanta, GA, United States, 12/15/10. https://doi.org/10.1109/CDC.2010.5717220

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AU - Martínez, Sonia

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N2 - We consider a multi-agent optimization problem where agents aim to cooperatively minimize a sum of local objective functions subject to a global inequality constraint and a global state constraint set. In contrast to existing papers, we do not require the objective, constraint functions, and state constraint sets to be convex. We propose a distributed approximate dual subgradient algorithm to enable agents to asymptotically converge to a pair of approximate primaldual solutions over dynamically changing network topologies. Convergence can be guaranteed provided that the Slater's condition and strong duality property are satisfied.

AB - We consider a multi-agent optimization problem where agents aim to cooperatively minimize a sum of local objective functions subject to a global inequality constraint and a global state constraint set. In contrast to existing papers, we do not require the objective, constraint functions, and state constraint sets to be convex. We propose a distributed approximate dual subgradient algorithm to enable agents to asymptotically converge to a pair of approximate primaldual solutions over dynamically changing network topologies. Convergence can be guaranteed provided that the Slater's condition and strong duality property are satisfied.

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