Convergence Analysis of Distributed Subgradient Methods over Random Networks
Author(s)Lobel, Ilan; Ozdaglar, Asuman E.
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We consider the problem of cooperatively minimizing the sum of convex functions, where the functions represent local objective functions of the agents. We assume that each agent has information about his local function, and communicate with the other agents over a time-varying network topology. For this problem, we propose a distributed subgradient method that uses averaging algorithms for locally sharing information among the agents. In contrast to previous works that make worst-case assumptions about the connectivity of the agents (such as bounded communication intervals between nodes), we assume that links fail according to a given stochastic process. Under the assumption that the link failures are independent and identically distributed over time (possibly correlated across links), we provide convergence results and convergence rate estimates for our subgradient algorithm.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Operations Research Center
46th Annual Allerton Conference on Communication, Control, and Computing, 2008
Institute of Electrical and Electronics Engineers
Lobel, I., and A. Ozdaglar. “Convergence analysis of distributed subgradient methods over random networks.” Communication, Control, and Computing, 2008 46th Annual Allerton Conference on. 2008. 353-360. © Copyright 2008 IEEE
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INSPEC Accession Number: 10479771