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Centralized and Distributed Online Learning for Sparse Time-Varying Optimization
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DOI：10.1109/TAC.2020.3010242.png)
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摘要
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The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Meanwhile, sparse time-varying optimization has emerged as a powerful tool to deal with widespread applications, ranging from dynamic compressed sensing to parsimonious system identification. In most of the literature on sparse time-varying problems, some prior information on the system's evolution is assumed to be available. In contrast, in this article, we propose an online learning approach, which does not employ a given model and is suitable for adversarial frameworks. Specifically, we develop centralized and distributed algorithms, and we theoretically analyze them in terms of dynamic regret, in an online learning perspective. Furthermore, we propose numerical experiments that illustrate their practical effectiveness.
Keyword:
Optimization
Heuristic algorithms
Convex functions
Time-varying systems
Target tracking
Signal processing algorithms
Distributed iterative soft thresholding (IST)
Douglas– Rachford (DR) splitting
dynamic regret
online learning
sparse optimization
time-varying systems
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期刊
IF:
7
论文数:
1.3W
被引数:
6.7W
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