RIP analysis for the weighted ℓr-ℓ1 minimization method

•The restricted isometry property (RIP) and high-order RIP analysis results for the weighted ℓr−ℓ1 minimization method are presented.•Through a novel decomposition of the objective function into a difference of two convex functions, the weighted ℓr−ℓ1 minimization problem is solved via the differenc...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Signal processing Jg. 202
1. Verfasser:	Zhou, Zhiyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2023
Schlagworte:	Compressive sensing Difference of convex functions algorithms Nonconvex sparse recovery Restricted isometry property Weighted [formula omitted] minimization Compressive sensing Restricted isometry property Difference of convex functions algorithms Nonconvex sparse recovery Weighted ℓr−ℓ1 minimization
ISSN:	0165-1684
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!