An Edge-based Stochastic Proximal Gradient Algorithm for Decentralized Composite Optimization

This paper investigates decentralized composite optimization problems involving a common non-smooth regularization term over an undirected and connected network. In the same situation, there exist lots of gradient-based proximal distributed methods, but most of them are only sublinearly convergent....

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Vydáno v:	International journal of control, automation, and systems Ročník 19; číslo 11; s. 3598 - 3610
Hlavní autoři:	Zhang, Ling, Yan, Yu, Wang, Zheng, Li, Huaqing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.11.2021 Springer Nature B.V 제어·로봇·시스템학회
Témata:	Algorithms Control Convergence Cost function Engineering Machine learning Mechatronics Optimization Performance degradation Regular Papers Regularization Robotics 제어계측공학 proximal-gradient method Decentralized composite optimization machine learning stochastic averaging gradient linear convergence
ISSN:	1598-6446, 2005-4092
On-line přístup:	Získat plný text
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