Scaling Up Coordinate Descent Algorithms for Large ${\ell }_1$ 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.

Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012)

References in corpus (3)

• Pathwise coordinate optimization
• GraphLab: A New Framework for Parallel Machine Learning
• Parallel Coordinate Descent for L1-Regularized Loss Minimization