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• W3201744755 abstract "This paper studies distributed optimization over the multi-agent network. We develop and analyze a novel accelerated distributed gradient descent method, termed as G-DGDlm, for gossip communication protocol. G-DGDlm uses gradient tracking technique and local memory. Specifically, each agent stores two past variables, namely, an estimate of the average gradient and an estimate of the optimal solution. G-DGDlm achieves linear convergence for strongly convex and smooth functions when the positive fixed stepsize is sufficiently small and the coefficient θ of past variables is $0 < theta leq 3 - 2sqrt 2 $. Compared to the related works, we remove the dependence of θ on global parameters and G-DGDlm is applicable to gossip communication protocol. Numerical experiments of distributed estimation in sensor networks show the faster convergence speed of G-DGDlm in comparison with the state-of-the-art methods." @default.
• W3201744755 created "2021-10-11" @default.
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• W3201744755 date "2021-07-26" @default.
• W3201744755 modified "2023-10-17" @default.
• W3201744755 title "An Accelerated Gossip-Based Distributed Gradient Method" @default.
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• W3201744755 doi "https://doi.org/10.23919/ccc52363.2021.9549539" @default.
• W3201744755 hasPublicationYear "2021" @default.
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