Distributed Randomized Gradient-Free Convex Optimization With Set Constraints Over Time-Varying Weight-Unbalanced Digraphs

This paper explores a class of distributed constrained convex optimization problems where the objective function is a sum of <inline-formula><tex-math notation="LaTeX">N</tex-math></inline-formula> convex local objective functions. These functions are characterized...

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Vydané v:	IEEE transactions on network science and engineering Ročník 12; číslo 2; s. 610 - 622
Hlavní autori:	Zhu, Yanan, Li, Qinghai, Li, Tao, Wen, Guanghui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Closed box Constraints Convergence Convergence rate Convex analysis Convex functions Convexity Directed graphs distributed multi-agent optimization Graph theory Linear programming Lipschitz condition Optimization Prediction algorithms randomized gradient-free Smoothing methods Smoothness Stochastic processes time-varying weight-unbalanced digraphs Vectors
ISSN:	2327-4697, 2334-329X
On-line prístup:	Získať plný text
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