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Kordas, G. (2015) A Neurodynamic Optimization Method for Recovery of Compressive Sensed Signals with Globally Converged Solution Approximating to L0 Minimization. IEEE Transactions on Neural Networks and Learning Systems, 26, 1363-1374.

has been cited by the following article:

• TITLE: A Generalized Elastic Net Regularization with Smoothed l0 Penalty

  AUTHORS: Sisu Li, Wanzhou Ye

  KEYWORDS: Sparse Vector, Compressed Sense, Elastic Net Regularization, l0 Minimization

  JOURNAL NAME: Advances in Pure Mathematics, Vol.7 No.1, January 24, 2017

  ABSTRACT: This paper presents an accurate and efficient algorithm for solving the generalized elastic net regularization problem with smoothed l0 penalty for recovering sparse vector. Finding the optimal solution to the unconstrained l0 minimization problem in the recovery of compressive sensed signals is an NP-hard problem. We proposed an iterative algorithm to solve this problem. We then prove that the algorithm is convergent based on algebraic methods. The numerical result shows the efficiency and the accuracy of the algorithm.