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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 317979, 8 pages
Research Article

Efficient LED-SAC Sparse Estimator Using Fast Sequential Adaptive Coordinate-Wise Optimization (LED-2SAC)

1Faculty of Electrical and Computer Engineering, University of Tabriz, 29 Bahman Avenue, Tabriz 51666-15813, Iran
2Department of Electrical and Computer Engineering, Ryerson University, Toronto, ON, Canada M5B 2K3

Received 7 October 2013; Accepted 29 December 2013; Published 6 February 2014

Academic Editor: Yue Wu

Copyright © 2014 T. Yousefi Rezaii et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Solving the underdetermined system of linear equations is of great interest in signal processing application, particularly when the underlying signal to be estimated is sparse. Recently, a new sparsity encouraging penalty function is introduced as Linearized Exponentially Decaying penalty, LED, which results in the sparsest solution for an underdetermined system of equations subject to the minimization of the least squares loss function. A sequential solution is available for LED-based objective function, which is denoted by LED-SAC algorithm. This solution, which aims to sequentially solve the LED-based objective function, ignores the sparsity of the solution. In this paper, we present a new sparse solution. The new method benefits from the sparsity of the signal both in the optimization criterion (LED) and its solution path, denoted by Sparse SAC (2SAC). The new reconstruction method denoted by LED-2SAC (LED-Sparse SAC) is consequently more efficient and considerably fast compared to the LED-SAC algorithm, in terms of adaptability and convergence rate. In addition, the computational complexity of both LED-SAC and LED-2SAC is shown to be of order , which is better than the other batch solutions like LARS. LARS algorithm has complexity of order , where is the dimension of the sparse signal and is the number of observations.