An Augmented Smoothing Method of L1 -norm Minimization and Its Implementation by Neural Network Model

Abstract

In this paper we propose an augmented smoothing function for nonlinear L1 -norm minimization problem and consider a global stability of a gradient-based neural network model to minimize the smoothing function. The numerical simulations show that our smoothing neural network finds successfully the global solution of the L1 -norm minimization problems considered in the simulation.