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A. Khajehnejad, W. Xu, S. Avestimher and B. Hassibi, “Analyzing Weight l1-Minimization for Sparse Recovery with Nonuniform Sparse Models,” IEEE Transaction on Signal Processing, Vol. 59, No. 5, 2011, pp. 1985-2001.
http://dx.doi.org/10.1109/TSP.2011.2107904

has been cited by the following article:

• TITLE: Numerical Studies of the Generalized l1Greedy Algorithm for Sparse Signals

  AUTHORS: Fangjun Arroyo, Edward Arroyo, Xiezhang Li, Jiehua Zhu, Jiehua Zhu

  KEYWORDS: Compressed Sensing; Gaussian Sparse Signals; l1-Minimization; Reweighted l1-Minimization; l1; Greedy Algorithm Generalized l1 Greedy Algorithm

  JOURNAL NAME: Advances in Computed Tomography, Vol.2 No.4, December 24, 2013

  ABSTRACT: The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.