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- W2896697879 abstract "This work derives and analyzes an online learning strategy for tracking the average of time-varying distributed signals by relying on randomized coordinate-descent updates. During each iteration, each agent selects or observes a random entry of the observation vector, and different agents may select different entries of their observations before engaging in a consultation step. Careful coordination of the interactions among agents is necessary to avoid bias and ensure convergence. We provide a convergence analysis for the proposed methods, and illustrate the results by means of simulations." @default.
- W2896697879 created "2018-10-26" @default.
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- W2896697879 date "2018-10-21" @default.
- W2896697879 modified "2023-10-14" @default.
- W2896697879 title "Dynamic Average Diffusion with randomized Coordinate Updates" @default.
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- W2896697879 doi "https://doi.org/10.48550/arxiv.1810.08901" @default.
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