Curvature-Exploiting Acceleration of Elastic Net Computations
Abstract
This paper introduces an efficient second-order method for solving the elastic net problem. Its key innovation is a computationally efficient technique for injecting curvature information in the optimization process which admits a strong theoretical performance guarantee. In particular, we show improved run time over popular first-order methods and quantify the speed-up in terms of statistical measures of the data matrix. The improved time complexity is the result of an extensive exploitation of the problem structure and a careful combination of second-order information, variance reduction techniques, and momentum acceleration. Beside theoretical speed-up, experimental results demonstrate great practical performance benefits of curvature information, especially for ill-conditioned data sets.
Cite
Text
Mai and Johansson. "Curvature-Exploiting Acceleration of Elastic Net Computations." International Conference on Machine Learning, 2019.Markdown
[Mai and Johansson. "Curvature-Exploiting Acceleration of Elastic Net Computations." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/mai2019icml-curvatureexploiting/)BibTeX
@inproceedings{mai2019icml-curvatureexploiting,
title = {{Curvature-Exploiting Acceleration of Elastic Net Computations}},
author = {Mai, Vien and Johansson, Mikael},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {4294-4303},
volume = {97},
url = {https://mlanthology.org/icml/2019/mai2019icml-curvatureexploiting/}
}