Quantized Zeroth-Order Gradient Tracking Algorithm for Distributed Nonconvex Optimization Under Polyak-Łojasiewicz Condition

This article focuses on distributed nonconvex optimization by exchanging information between agents to minimize the average of local nonconvex cost functions. The communication channel between agents is normally constrained by limited bandwidth, and the gradient information is typically unavailable....

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 54; H. 10; S. 5746 - 5758
Hauptverfasser:	Xu, Lei, Yi, Xinlei, Deng, Chao, Shi, Yang, Chai, Tianyou, Yang, Tao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.10.2024
Schlagworte:	Convergence Cost function Deep learning Distributed algorithms Gradient methods Gradient tracking algorithm linear convergence nonconvex optimization Quantization (signal) uniform quantizer zeroth-order algorithm
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
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