An Augmented Smoothing Method of L1 -norm Minimization and Its Implementation by Neural Network Model
Optimization and Control
2012-07-10 v1
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.
Keywords
Cite
@article{arxiv.1207.1931,
title = {An Augmented Smoothing Method of L1 -norm Minimization and Its Implementation by Neural Network Model},
author = {Yunchol Jong},
journal= {arXiv preprint arXiv:1207.1931},
year = {2012}
}