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      A Distributed Online Convex Optimization Algorithm with Improved Dynamic Regret

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          Abstract

          In this paper, we consider the problem of distributed online convex optimization, where a network of local agents aim to jointly optimize a convex function over a period of multiple time steps. The agents do not have any information about the future. Existing algorithms have established dynamic regret bounds that have explicit dependence on the number of time steps. In this work, we show that we can remove this dependence assuming that the local objective functions are strongly convex. More precisely, we propose a gradient tracking algorithm where agents jointly communicate and descend based on corrected gradient steps. We verify our theoretical results through numerical experiments.

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          Distributed Subgradient Methods for Multi-Agent Optimization

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            Logarithmic regret algorithms for online convex optimization

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              Distributed optimization in sensor networks

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                Author and article information

                Journal
                12 November 2019
                Article
                1911.05050
                62772bdb-157c-4ee6-9156-fb07f9a9a067

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                math.OC cs.LG

                Numerical methods,Artificial intelligence
                Numerical methods, Artificial intelligence

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