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...

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Bibliographic Details
Published in:Signal processing Vol. 202
Main Author: Zhou, Zhiyong
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2023
Subjects:
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 Access:Get full text
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Summary:•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 difference of convex functions algorithms (DCA) directly.•Numerical experiments show that the DCA based weighted ℓr−ℓ1 minimization method gives satisfactory results in sparse recovery no matter whether the measurement matrix is coherent or not.•For highly coherent measurements, the proposed method even outperforms the state-of-art ℓ1−ℓ2 minimization method. The weighted ℓr−ℓ1 minimization method with 0<r≤1 largely generalizes the classical ℓr minimization method and achieves very good performance in compressive sensing. However, its restricted isometry property (RIP) and high-order RIP analysis results remain unknown. In this paper, we fill in this gap by adopting newly developed analysis tools. Moreover, through a novel decomposition of the objective function into a difference of two convex functions, we propose to solve the weighted ℓr−ℓ1 minimization problem via the difference of convex functions algorithms (DCA) directly. Numerical experiments show that our DCA based weighted ℓr−ℓ1 minimization method gives satisfactory results in sparse recovery no matter whether the measurement matrix is coherent or not. For highly coherent measurements, our proposed method even outperforms the state-of-art ℓ1−ℓ2 minimization method.
ISSN:0165-1684
DOI:10.1016/j.sigpro.2022.108754