Acta mathematica scientia,Series A ›› 2022, Vol. 42 ›› Issue (3): 941-956.doi: 10.1007/s10473-022-0308-4

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

Haifeng LI, Jing ZHANG   

  1. Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, College of Mathematics and Information Science, Henan Normal University, Xinxiang, 453007, China
  • Received:2020-09-02 Revised:2021-05-28 Online:2022-06-26 Published:2022-06-24
  • Contact: Haifeng Li,E-mail:lihaifengxx@126.com E-mail:lihaifengxx@126.com
  • Supported by:
    This work was partially supported by the National Natural Science Foundation of China (61907014, 11871248, 11701410, 61901160), Youth Science Foundation of Henan Normal University (2019QK03).

Abstract: 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 $\mathbf{x}\in\mathbb{R}^n$. We show that MOLS provides stable reconstruction of all $K$-sparse signals $\mathbf{x}$ from $\mathbf{y}=\mathbf{A}\mathbf{x}+\mathbf{w}$ in $\lceil\frac{6K}{M}\rceil$ iterations when the matrix $\mathbf{A}$ satisfies the restricted isometry property (RIP) with isometry constant $\delta_{7K}\leq0.094$. Compared with the existing results, our sufficient condition is not related to the sparsity level $K$.

Key words: Sparse signal recovery, multiple orthogonal least squares (MOLS), sufficient condition, restricted isometry property (RIP)

CLC Number: 

  • 94A12
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