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- W2964165783 abstract "This paper develops a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies. The resulting exact diffusion strategy is shown in Part II of this paper to have a wider stability range and superior convergence performance than the EXTRA strategy. The exact diffusion method is applicable to locally balanced left-stochastic combination matrices which, compared to the conventional doubly stochastic matrix, are more general and able to endow the algorithm with faster convergence rates, more flexible step-size choices, and improved privacy-preserving properties. The derivation of the exact diffusion strategy relies on reformulating the aggregate optimization problem as a penalized problem and resorting to a diagonally weighted incremental construction. Detailed stability and convergence analyses are pursued in Part II of this paper and are facilitated by examining the evolution of the error dynamics in a transformed domain. Numerical simulations illustrate the theoretical conclusions." @default.
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- W2964165783 date "2019-02-01" @default.
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- W2964165783 title "Exact Diffusion for Distributed Optimization and Learning—Part I: Algorithm Development" @default.
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- W2964165783 doi "https://doi.org/10.1109/tsp.2018.2875898" @default.
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