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The goal of decentralized optimization over a network is to optimize a global objective formed by a sum of local (possibly non-smooth) convex functions using only local computation and communication. It arises in various application domains, including distributed tracking and localization, multiagent co-ordination, estimation in sensor networks, and large-scale machine learning. The authors develop and analyze distributed algorithms based on dual subgradient averaging, and they provide sharp bounds on their convergence rates as a function of the network size and topology.
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