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Distributed multiagent learning with a broadcast adaptive subgradient method

Distributed multiagent learning with a broadcast adaptive subgradient method
Distributed multiagent learning with a broadcast adaptive subgradient method
Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function being minimized (examples of such applications include, among others, coordinated source localization, distributed adaptive filtering, control, and coordination). Given this observation, we propose a new non-hierarchical decentralized algorithm for the asymptotic minimization of possibly time-varying convex functions. In our method each agent has knowledge of a time-varying local cost function, and the objective is to minimize asymptotically a global cost function defined by the sum of the local functions. At each iteration of our algorithm, agents improve their estimates of a minimizer of the global function by applying a particular version of the adaptive projected subgradient method to their local functions. Then the agents exchange and mix their improved estimates using a probabilistic model based on recent results in weighted average consensus algorithms. The resulting algorithm is provably optimal and reproduces as particular cases many existing algorithms (such as consensus algorithms and recent methods based on the adaptive projected subgradient method). To illustrate one possible application, we show how our algorithm can be applied to coordinated acoustic source localization in sensor networks
decentralized convex optimization, distributed computing, consensus, acoustic source localization
1039-1046
International Foundation for Autonomous Agents and Multiagent Systems
Cavalcante, R. L. G.
7c2e5089-c076-4287-8ba1-26c98bf30a14
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Yamada, I.
472a0cab-1376-424e-9f08-676470cafc23
Cavalcante, R. L. G.
7c2e5089-c076-4287-8ba1-26c98bf30a14
Rogers, A.
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Yamada, I.
472a0cab-1376-424e-9f08-676470cafc23

Cavalcante, R. L. G., Rogers, A., Jennings, N. R. and Yamada, I. (2010) Distributed multiagent learning with a broadcast adaptive subgradient method. In Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems. pp. 1039-1046 .

Record type: Conference or Workshop Item (Paper)

Abstract

Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function being minimized (examples of such applications include, among others, coordinated source localization, distributed adaptive filtering, control, and coordination). Given this observation, we propose a new non-hierarchical decentralized algorithm for the asymptotic minimization of possibly time-varying convex functions. In our method each agent has knowledge of a time-varying local cost function, and the objective is to minimize asymptotically a global cost function defined by the sum of the local functions. At each iteration of our algorithm, agents improve their estimates of a minimizer of the global function by applying a particular version of the adaptive projected subgradient method to their local functions. Then the agents exchange and mix their improved estimates using a probabilistic model based on recent results in weighted average consensus algorithms. The resulting algorithm is provably optimal and reproduces as particular cases many existing algorithms (such as consensus algorithms and recent methods based on the adaptive projected subgradient method). To illustrate one possible application, we show how our algorithm can be applied to coordinated acoustic source localization in sensor networks

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Submitted date: 13 October 2009
Published date: May 2010
Additional Information: Event Dates: May 10-14, 2010
Venue - Dates: Ninth International Conference on Autonomous Agents and Multiagent Systems, 2010-05-10 - 2010-05-14
Keywords: decentralized convex optimization, distributed computing, consensus, acoustic source localization

Identifiers

Local EPrints ID: 72297
URI: https://eprints.soton.ac.uk/id/eprint/72297
PURE UUID: 2e6a0d77-b657-41b7-b9af-e3f59bc5c79f

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Date deposited: 08 Feb 2010
Last modified: 25 Feb 2019 17:32

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