A NEW SUFFICIENT CONDITION FOR SPARSE RECOVERY WITH MULTIPLE ORTHOGONAL LEAST SQUARES

作     者：李海锋 张静 Haifeng LI;Jing ZHANG

作者机构：Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal ControlCollege of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China 

出 版 物：《Acta Mathematica Scientia》 (数学物理学报（B辑英文版）)

年 卷 期：2022年第42卷第3期

页      面：941-956页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 0701[理学-数学] 

基　　金：supported by the National Natural Science Foundation of China(61907014,11871248,11701410,61901160) Youth Science Foundation of Henan Normal University(2019QK03). 

主　　题：Sparse signal recovery multiple orthogonal least squares(MOLS) sufficient condition restricted isometry property(RIP) 

摘      要：A greedy algorithm used for the recovery of sparse signals,multiple orthogonal least squares(MOLS)have recently attracted quite a big of attention.In this paper,we consider the number of iterations required for the MOLS algorithm for recovery of a K-sparse signal x∈R^(n).We show that MOLS provides stable reconstruction of all K-sparse signals x from y=Ax+w in|6K/ M|iterations when the matrix A satisfies the restricted isometry property(RIP)with isometry constantδ_(7K)≤0.094.Compared with the existing results,our sufficient condition is not related to the sparsity level K.