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Decentralized MultiAgent Optimization via Dual Decomposition 
Abstract 
We study a distributed multiagent optimiz … We study a distributed multiagent 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 multihop 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  ttm11ifac + 
Title  Decentralized MultiAgent Optimization via Dual Decomposition + 
Type  Preprint + 
Categories  Papers 
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20 May 2016 08:17:38 + 
URL This property is a special property in this wiki.

http://www.cds.caltech.edu/~murray/preprints/ttm11ifac_s.pdf + 
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Decentralized MultiAgent Optimization via Dual Decomposition +  Title 
