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.