Scaling Up Coordinate Descent Algorithms for Large $\ell_1$ Regularization Problems
Machine Learning
2012-07-04 v1 Distributed, Parallel, and Cluster Computing
Machine Learning
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.
Keywords
Cite
@article{arxiv.1206.6409,
title = {Scaling Up Coordinate Descent Algorithms for Large $\ell_1$ Regularization Problems},
author = {Chad Scherrer and Mahantesh Halappanavar and Ambuj Tewari and David Haglin},
journal= {arXiv preprint arXiv:1206.6409},
year = {2012}
}
Comments
Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)