Compressed Gradient Tracking Algorithm for Distributed Aggregative Optimization

This article is devoted to addressing the distributed aggregative optimization (DAO) problem via compressed gradient tracking algorithms, where the cost function of each agent relies on the aggregation of other agents' decisions as well as its own decision. To this end, a new kind of the distri...

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Vydané v:	IEEE transactions on automatic control Ročník 69; číslo 10; s. 6576 - 6591
Hlavní autori:	Chen, Liyuan, Wen, Guanghui, Liu, Hongzhe, Yu, Wenwu, Cao, Jinde
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.10.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Communication compression Compressors Convergence Cost function distributed aggregative optimization (DAO) Gradient methods gradient tracking algorithm Linear matrix inequalities Machine learning algorithms Optimization Prediction algorithms Time-varying channels time-varying graph Tracking
ISSN:	0018-9286, 1558-2523
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