In this paper we extend the work of Friedman et al. [2007] and develop fast algorithms for fitting generalized linear models with elastic-net penalties. In particular, our models include regression, two-class logistic regression, and multinomial regression problems. Our algorithms can work on very large datasets, and can take advantage of sparsity in the feature set. We provide a publicly available R package glmnet. We do not revisit the well-established convergence properties of coordinate descent in convex problems [Tseng, 2001] in this article.
