A Distributed Nesterov-Like Gradient Tracking Algorithm for Composite Constrained Optimization

This paper focuses on the constrained optimization problem where the objective function is composed of smooth (possibly nonconvex) and nonsmooth parts. The proposed algorithm integrates the successive convex approximation (SCA) technique with the gradient tracking mechanism that aims at achieving a...

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Vydané v:	IEEE transactions on signal and information processing over networks Ročník 9; s. 1 - 13
Hlavní autori:	Zheng, Lifeng, Li, Huaqing, Li, Jun, Wang, Zheng, Lu, Qingguo, Shi, Yawei, Wang, Huiwei, Dong, Tao, Ji, Lianghao, Xia, Dawen
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Approximation algorithms Convergence Distributed algorithms distributed optimization gradient tracking Information processing Linear programming Manganese Momentum Nesterov method nonconvex optimization Optimization Successive convex approximation (SCA) Tracking Upper bounds
ISSN:	2373-776X, 2373-7778
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