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Decentralized Multi-Agent Optimization via Dual Decomposition
Abstract We study a distributed multi-agent optimiz
We study a distributed multi-agent optimization problem of minimizing the sum of convex objective functions. A new decentralized optimization algorithm is introduced, based on dual decomposition, together with the subgradient method for finding the optimal solution. The iterative algorithm is implemented on a multi-hop network and is designed to handle communication delays. The convergence of the algorithm is proved for communication networks with bounded delays. An explicit bound, which depends on the communication delays, on the convergence rate is given. A numerical comparison with a decentralized primal algorithm shows that the dual algorithm converges faster, with less communication.
converges faster, with less communication.  +
Authors Håkan Terelius, Ufuk Topcu, Richard M Murray  +
ID 2010k  +
Source IFAC World Congress, 2011 (Submitted)  +
Tag ttm11-ifac  +
Title Decentralized Multi-Agent Optimization via Dual Decomposition +
Type Preprint  +
Categories Papers
Modification date
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20 May 2016 08:17:38  +
URL
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http://www.cds.caltech.edu/~murray/preprints/ttm11-ifac_s.pdf  +
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Decentralized Multi-Agent Optimization via Dual Decomposition + Title
 

 

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