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Dynamic Average Diffusion With Randomized Coordinate Updates
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OA
AI
DOI：10.1109/TSIPN.2019.2942191.png)
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Abstract
En 中文
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.
Keywords:
Heuristic algorithms
Indexes
Convergence
Optimization
Information processing
Distributed algorithms
Network topology
Dynamic average diffusion
consensus
push-sum algorithm
coordinate descent
exact diffusion
Journal
IF:
4.9
Papers:
739
Citations:
1.9K
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