A conjugate subgradient algorithm with adaptive preconditioning for LASSO minimization
Data Analysis, Statistics and Probability
2015-06-30 v2 Numerical Analysis
Abstract
This paper describes a new efficient conjugate subgradient algorithm which minimizes a convex function containing a least squares fidelity term and an absolute value regularization term. This method is successfully applied to the inversion of ill-conditioned linear problems, in particular for computed tomography with the dictionary learning method. A comparison with other state-of-art methods shows a significant reduction of the number of iterations, which makes this algorithm appealing for practical use.
Cite
@article{arxiv.1506.07730,
title = {A conjugate subgradient algorithm with adaptive preconditioning for LASSO minimization},
author = {Alessandro Mirone and Pierre Paleo},
journal= {arXiv preprint arXiv:1506.07730},
year = {2015}
}