Decentralized Multi-Agent Optimization via Dual Decomposition
2011 (English)Conference paper (Refereed)
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, and with less communication.
Place, publisher, year, edition, pages
2011. 11245-11251 p.
, IFAC Proceedings Volumes (IFAC-PapersOnline), ISSN 1474-6670 ; 18
Distributed multi-agent systems, Convex optimization, Dual decomposition
IdentifiersURN: urn:nbn:se:kth:diva-46507DOI: 10.3182/20110828-6-IT-1002.01959ScopusID: 2-s2.0-84866753343ISBN: 9783902661937OAI: oai:DiVA.org:kth-46507DiVA: diva2:453810
18th IFAC World Congress, 28 August 2011 through 2 September 2011, Milano, Italy
QC 201111242011-11-242011-11-032015-01-27Bibliographically approved