Sorted L1/L2 Minimization for Sparse Signal Recovery

Wang，Chao1,2; Yan，Ming3; Yu，Junjie1

2024-05-01

10.1007/s10915-024-02497-2

Journal of Scientific Computing   影响因子和分区

0885-7474

1573-7691

This paper introduces a novel approach for recovering sparse signals using sorted L/L minimization. The proposed method assigns higher weights to indices with smaller absolute values and lower weights to larger values, effectively preserving the most significant contributions to the signal while promoting sparsity. We present models for both noise-free and noisy scenarios, and rigorously prove the existence of solutions for each case. To solve these models, we adopt a linearization approach inspired by the difference of convex functions algorithm. Our experimental results demonstrate the superiority of our method over state-of-the-art approaches in sparse signal recovery across various circumstances, particularly in support detection.

49N45 65K10 90C05 90C26 L1/L2 minimization Non-convex optimization Sparsity Support detection

SCI ; EI

英语

第一 ; 通讯

MATHEMATICS

2-s2.0-85188304171

Scopus

1.Department of Statistics and Data Science,Southern University of Science and Technology,Shenzhen,Guangdong Province,518005,China
2.National Centre for Applied Mathematics Shenzhen,Shenzhen,Guangdong Province,518055,China
3.School of Data Science,The Chinese University of Hong Kong,Shenzhen (CUHK-Shenzhen),Shenzhen,518172,China

Wang，Chao,Yan，Ming,Yu，Junjie. Sorted L1/L2 Minimization for Sparse Signal Recovery[J]. Journal of Scientific Computing,2024,99(2).

Wang，Chao,Yan，Ming,&Yu，Junjie.(2024).Sorted L1/L2 Minimization for Sparse Signal Recovery.Journal of Scientific Computing,99(2).

Wang，Chao,et al."Sorted L1/L2 Minimization for Sparse Signal Recovery".Journal of Scientific Computing 99.2(2024).