SPARSE RECOVERY BASED ON THE GENERALIZED ERROR FUNCTION  

作　　者：Zhiyong Zhou 

机构地区：[1]Department of Statistics and Data Science,and Institute of Digital Finance,Hangzhou City University,Hangzhou 310015,China

出　　处：《Journal of Computational Mathematics》2024年第3期679-704,共26页计算数学（英文）

基　　金：supported by the Zhejiang Provincial Natural Science Foundation of China under grant No.LQ21A010003.

摘　　要：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■_(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.

关 键 词：Sparse recovery Generalized error function Nonconvex regularization Itera-tive reweighted Li Difference of convex functions algorithms 

分 类 号：O17[理学—数学]