Abstract
AbstractIn this paper, we study a nonconvex, nonsmooth, and non-Lipschitz weighted $l_{p}-l_{q}$
l
p
−
l
q
($0< p\leq 1$
0
<
p
≤
1
, $1< q\leq 2$
1
<
q
≤
2
; $0\leq \alpha \leq 1$
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 block-sparse signals and rank minimization. We also obtain the theoretical bound for the block-sparse signals and rank minimization when measurements are degraded by noise.
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Discrete Mathematics and Combinatorics,Analysis
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