Distributed Subgradient Methods

We study a distributed computation model for op- timizing a sum of convex objective functions corresponding to multiple agents.Forsolving this(not necessarilysmooth)optimiza- tion problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate es- timates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.

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