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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Veröffentlicht in:	IEEE transactions on network science and engineering Jg. 12; H. 2; S. 610 - 622
Hauptverfasser:	Zhu, Yanan, Li, Qinghai, Li, Tao, Wen, Guanghui
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.03.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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