Block-sparse recovery and rank minimization using a weighted lp−lq model

In this paper, we study a nonconvex, nonsmooth, and non-Lipschitz weighted l p − l q ( 0 < p ≤ 1 , 1 < q ≤ 2 ; 0 ≤ α ≤ 1 ) norm as a nonconvex metric to recover block-sparse signals and rank-minimization problems. Using block-RIP and matrix-RIP conditions, we obtain exact recovery results for...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of inequalities and applications 2023-02, Vol.2023 (1), p.29
Hauptverfasser:	Nigam, H. K., Yadav, Saroj
Format:	Artikel
Sprache:	eng
Schlagworte:	Analysis Applications of Mathematics Machine learning Mathematics Mathematics and Statistics Optimization Recovery Signal processing
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!