A RANDOMIZED INCREMENTAL SUBGRADIENT METHOD FOR DISTRIBUTED OPTIMIZATION IN NETWORKED SYSTEMS
2009 (English)In: SIAM Journal on Optimization, ISSN 1052-6234, E-ISSN 1095-7189, Vol. 20, no 3, 1157-1170 p.Article in journal (Refereed) Published
We present an algorithm that generalizes the randomized incremental subgradient method with fixed stepsize due to Nedic and Bertsekas [SIAM J. Optim., 12 (2001), pp. 109-138]. Our novel algorithm is particularly suitable for distributed implementation and execution, and possible applications include distributed optimization, e.g., parameter estimation in networks of tiny wireless sensors. The stochastic component in the algorithm is described by a Markov chain, which can be constructed in a distributed fashion using only local information. We provide a detailed convergence analysis of the proposed algorithm and compare it with existing, both deterministic and randomized, incremental subgradient methods.
Place, publisher, year, edition, pages
2009. Vol. 20, no 3, 1157-1170 p.
convex programming, subgradient optimization, distributed optimization, Markov chain
IdentifiersURN: urn:nbn:se:kth:diva-46958DOI: 10.1137/08073038XISI: 000277836500002ScopusID: 2-s2.0-73249115078OAI: oai:DiVA.org:kth-46958DiVA: diva2:454425
QC 201111072011-11-072011-11-072012-03-20Bibliographically approved