Trust-Region Methods for Sparse Relaxation
Numerical Analysis
2016-03-01 v1
Abstract
In this paper, we solve the l2-l1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and apply a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving the computational time to converge.
Cite
@article{arxiv.1602.08813,
title = {Trust-Region Methods for Sparse Relaxation},
author = {Lasith Adhikari and Jennifer B. Erway and Shelby Lockhart and Roummel F. Marcia},
journal= {arXiv preprint arXiv:1602.08813},
year = {2016}
}
Comments
Department of Mathematics, Wake Forest University, Technical Report 2016-1