Year: 2024
Author: Zhiyong Zhou
Journal of Computational Mathematics, Vol. 42 (2024), Iss. 3 : pp. 679–704
Abstract
In this paper, we offer a new sparse recovery strategy based on the generalized error function. The introduced penalty function involves both the shape and the scale parameters, making it extremely flexible. For both constrained and unconstrained models, the theoretical analysis results in terms of the null space property, the spherical section property and the restricted invertibility factor are established. The practical algorithms via both the iteratively reweighted $ℓ_1$ and the difference of convex functions algorithms are presented. Numerical experiments are carried out to demonstrate the benefits of the suggested approach in a variety of circumstances. Its practical application in magnetic resonance imaging (MRI) reconstruction is also investigated.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.2204-m2021-0288
Journal of Computational Mathematics, Vol. 42 (2024), Iss. 3 : pp. 679–704
Published online: 2024-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 26
Keywords: Sparse recovery Generalized error function Nonconvex regularization Iterative reweighted L1 Difference of convex functions algorithms.
Author Details
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