AISTATS 2018poster0 citations
HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step
Yuting Ye, Lihua Lei, Cheng Ju
Abstract
In this article, we develop and analyze a homotopy continuation method, referred to as HONES , for solving the sequential generalized projections in Online Newton Step (Hazan et al., 2006b), as well as the generalized problem known as sequential standard quadratic programming. HONES is fast, tuning-free, error-free (up to machine error) and adaptive to the solution sparsity. This is confirmed by both careful theoretical analysis and extensive experiments on both synthetic and real data.
BibTeX
@InProceedings{pmlr-v84-ye18a,
title = {HONES: A Fast and Tuning-free Homotopy Method For Online Newton Step},
author = {Ye, Yuting and Lei, Lihua and Ju, Cheng},
booktitle = {Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics},
pages = {2008--2017},
year = {2018},
editor = {Storkey, Amos and Perez-Cruz, Fernando},
volume = {84},
series = {Proceedings of Machine Learning Research},
month = {09--11 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v84/ye18a/ye18a.pdf},
url = {https://proceedings.mlr.press/v84/ye18a.html},
abstract = {In this article, we develop and analyze a homotopy continuation method, referred to as HONES , for solving the sequential generalized projections in Online Newton Step (Hazan et al., 2006b), as well as the generalized problem known as sequential standard quadratic programming. HONES is fast, tuning-free, error-free (up to machine error) and adaptive to the solution sparsity. This is confirmed by both careful theoretical analysis and extensive experiments on both synthetic and real data.}
}