Decentralized online optimization with heterogeneous data sources

Abstract

We consider stochastic optimization problems in decentralized settings, where a network of agents aims to learn decision variables which are optimal in terms of a global objective which depends on possibly heterogeneous streaming observations received at each node. Consensus optimization techniques implicitly operate on the hypothesis that each node aims to… (More)
DOI: 10.1109/GlobalSIP.2016.7905895

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