A Distributed Proximal Alternating Direction Multiplier Method for Multiblock Nonsmooth Composite Optimization

In this article, we address a composite optimization problem in a distributed network. Each agent in the network possesses a private local convex function consisting of a differentiable term, a nonsmooth term, and a nonsmooth term combined with a linear operator. The objective is to minimize the sum...

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Vydáno v:	IEEE transactions on control of network systems Ročník 12; číslo 1; s. 202 - 215
Hlavní autoři:	Zhou, Yuan, Guo, Luyao, Shi, Xinli, Cao, Jinde
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Artificial neural networks Asynchronous algorithm Convergence Convex functions distributed composite optimization Linear operators Linear programming Machine learning algorithms Multipliers Network topologies Operators (mathematics) Optimization proximal alternating direction multiplier method (ADMM) Vectors
ISSN:	2325-5870, 2372-2533
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