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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Vydané v:	IEEE transactions on cybernetics Ročník 54; číslo 10; s. 5746 - 5758
Hlavní autori:	Xu, Lei, Yi, Xinlei, Deng, Chao, Shi, Yang, Chai, Tianyou, Yang, Tao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.10.2024
Predmet:	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
On-line prístup:	Získať plný text
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