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Distributed Subgradient Methods for Multi-Agent Optimization

2009; Institute of Electrical and Electronics Engineers; Volume: 54; Issue: 1 Linguagem: Inglês

10.1109/tac.2008.2009515

2334-3303

Angelia Nedić, Asuman Ozdaglar,

Sparse and Compressive Sensing Techniques

We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization 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 estimates 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.