Scaling up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems
Abstract
We present a generic framework for parallel coordinate descent (CD) algorithms that includes, as special cases, the original sequential algorithms Cyclic CD and Stochastic CD, as well as the recent parallel Shotgun algorithm. We introduce two novel parallel algorithms that are also special cases--Thread-Greedy CD and Coloring-Based CD--and give performance measurements for an OpenMP implementation of these.
Cite
Text
Scherrer et al. "Scaling up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems." International Conference on Machine Learning, 2012.Markdown
[Scherrer et al. "Scaling up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/scherrer2012icml-scaling/)BibTeX
@inproceedings{scherrer2012icml-scaling,
title = {{Scaling up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems}},
author = {Scherrer, Chad and Halappanavar, Mahantesh and Tewari, Ambuj and Haglin, David},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/scherrer2012icml-scaling/}
}