- Journal Home
- Volume 18 - 2025
- Volume 17 - 2024
- Volume 16 - 2023
- Volume 15 - 2022
- Volume 14 - 2021
- Volume 13 - 2020
- Volume 12 - 2019
- Volume 11 - 2018
- Volume 10 - 2017
- Volume 9 - 2016
- Volume 8 - 2015
- Volume 7 - 2014
- Volume 6 - 2013
- Volume 5 - 2012
- Volume 4 - 2011
- Volume 3 - 2010
- Volume 2 - 2009
- Volume 1 - 2008
Numer. Math. Theor. Meth. Appl., 9 (2016), pp. 185-192.
Published online: 2016-09
Cited by
- BibTex
- RIS
- TXT
In this paper, a new stopping rule is proposed for orthogonal multi-matching pursuit (OMMP). We show that, for $ℓ_2$ bounded noise case, OMMP with the new stopping rule can recover the true support of any $K$-sparse signal $x$ from noisy measurements $y = Φx + e$ in at most $K$ iterations, provided that all the nonzero components of $x$ and the elements of the matrix $Φ$ satisfy certain requirements. The proposed method can improve the existing result. In particular, for the noiseless case, OMMP can exactly recover any $K$-sparse signal under the same RIP condition.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2016.m1424}, url = {http://global-sci.org/intro/article_detail/nmtma/12373.html} }In this paper, a new stopping rule is proposed for orthogonal multi-matching pursuit (OMMP). We show that, for $ℓ_2$ bounded noise case, OMMP with the new stopping rule can recover the true support of any $K$-sparse signal $x$ from noisy measurements $y = Φx + e$ in at most $K$ iterations, provided that all the nonzero components of $x$ and the elements of the matrix $Φ$ satisfy certain requirements. The proposed method can improve the existing result. In particular, for the noiseless case, OMMP can exactly recover any $K$-sparse signal under the same RIP condition.