Event-Triggered Proximal Online Gradient Descent Algorithm for Parameter Estimation

The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mech...

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Vydáno v:IEEE transactions on signal processing Ročník 72; s. 2594 - 2606
Hlavní autoři: Zhou, Yaoyao, Chen, Gang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Convex functions
Distributed parameter estimation
dynamic regret
Dynamic scheduling
Estimation
event-triggered mechanism
Heuristic algorithms
online convex optimization
Parameter estimation
Performance degradation
proximal algorithm
Sensors
Signal processing algorithms
ISSN:1053-587X, 1941-0476
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Shrnutí:The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mechanism is introduced and the novel event-triggered proximal online gradient descent algorithm (EPOGDA) is proposed. The analysis shows that if the event-triggered threshold converges to zero as time tends to infinity and the cumulative difference between consecutive optimal values is sublinear, the dynamic regret of EPOGDA is sublinear. Further, we extend the proposed EPOGDA to the gradient-free scenarios, where the gradients are estimated using the Gaussian smoothed gradient estimator (GSGE). The GSGE-EPOGDA is presented and analyzed, which does not lead to performance degradation as compared to EPOGDA. Finally, the advantages of EPOGDA and GSGE-EPOGDA are verified on a distributed multi-sensor network.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2024.3400453