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Distributed Subgradient Methods for Multi-Agent Optimization
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DOI：10.1109/TAC.2008.2009515.png)
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Abstract
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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.
Keywords:
Convex optimization
cooperative control
distributed optimization
multi-agent network
subgradient method
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