Primal-Dual Fixed Point Algorithms Based on Adapted Metric for Distributed Optimization

This article considers distributed optimization by a group of agents over an undirected network. The objective is to minimize the sum of a twice differentiable convex function and two possibly nonsmooth convex functions, one of which is composed of a bounded linear operator. A novel distributed prim...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 34; číslo 6; s. 2923 - 2937
Hlavní autori:	Li, Huaqing, Zheng, Zuqing, Lu, Qingguo, Wang, Zheng, Gao, Lan, Wu, Guo-Cheng, Ji, Lianghao, Wang, Huiwei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.06.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adapted metric method Algorithms Approximation algorithms completely distributed Convergence Convex functions Fixed points (mathematics) Iterative methods Linear operators Mathematical analysis Mathematical models Measurement nonsmooth convex function Operators (mathematics) Optimization Prediction algorithms primal–dual fixed point algorithm randomized asynchronous iterative Signal processing algorithms
ISSN:	2162-237X, 2162-2388, 2162-2388
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