Geodesic
Sparse recovery for compressive sensing via weighted Lp−q model
Filomat, Tome 36 (2022) no. 14, p. 4709

Voir la notice de l'article provenant de la source eLibrary of Mathematical Institute of the Serbian Academy of Sciences and Arts

DOI

In this paper, we study weighted L p−q minimization model which comprises non-smooth, non-convex and non-Lipschitz quasi-norm L p (0 p ≤ 1) and L q (1 q ≤ 2) for recovering sparse signals. Based on the restricted isometry property (RIP) condition, we obtain exact sparse signal recovery result. We also obtain the theoretical bound for the weighted L p−q minimization model when measurements are depraved by the noises.
DOI : 10.2298/FIL2214709N
Classification : 90C26
Keywords: Non-convexity, compressed sensing, restricted isometry property (RIP), sparse signal 
H K Nigam; Saroj Yadav. Sparse recovery for compressive sensing via weighted Lp−q model. Filomat, Tome 36 (2022) no. 14, p. 4709 . doi: 10.2298/FIL2214709N
@article{10_2298_FIL2214709N,
     author = {H K Nigam and Saroj Yadav},
     title = {Sparse recovery for compressive sensing via weighted {Lp\ensuremath{-}q} model},
     journal = {Filomat},
     pages = {4709 },
     year = {2022},
     volume = {36},
     number = {14},
     doi = {10.2298/FIL2214709N},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.2298/FIL2214709N/}
}
TY  - JOUR
AU  - H K Nigam
AU  - Saroj Yadav
TI  - Sparse recovery for compressive sensing via weighted Lp−q model
JO  - Filomat
PY  - 2022
SP  - 4709 
VL  - 36
IS  - 14
UR  - http://geodesic.mathdoc.fr/articles/10.2298/FIL2214709N/
DO  - 10.2298/FIL2214709N
LA  - en
ID  - 10_2298_FIL2214709N
ER  - 
%0 Journal Article
%A H K Nigam
%A Saroj Yadav
%T Sparse recovery for compressive sensing via weighted Lp−q model
%J Filomat
%D 2022
%P 4709 
%V 36
%N 14
%U http://geodesic.mathdoc.fr/articles/10.2298/FIL2214709N/
%R 10.2298/FIL2214709N
%G en
%F 10_2298_FIL2214709N

Cité par Sources :