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• W2963361330 abstract "Distributed optimization aims to optimize a global objective formed by a sum of coupled local convex functions over a graph via only local computation and communication. In this letter, we propose the Bregman parallel direction method of multipliers (PDMM) based on a generalized averaging step named mirror averaging. We establish the global convergence and O(1/T) convergence rate of the Bregman PDMM, along with its O(n/ ln n) improvement over existing PDMM, where T denotes the number of iterations and n the dimension of solution variable. In addition, we can enhance its performance by optimizing the spectral gap of the averaging matrix. We demonstrate our results via a numerical example." @default.
• W2963361330 created "2019-07-30" @default.
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• W2963361330 date "2018-04-01" @default.
• W2963361330 modified "2023-10-18" @default.
• W2963361330 title "Bregman Parallel Direction Method of Multipliers for Distributed Optimization via Mirror Averaging" @default.
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• W2963361330 doi "https://doi.org/10.1109/lcsys.2018.2833116" @default.
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