2024/05/14 by James Yang, Trevor Hastie, Yang, James +1 · 2 citations
Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mathematical Software (cs.MS) #Software Engineering (cs.SE) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.2405.08631
openalex publication_date 2024/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We develop fast and scalable algorithms based on block-coordinate descent to solve the group lasso and the group elastic net for generalized linear models along a regularization path. Special attention is given when the loss is the usual least squares loss (Gaussian loss). We show that each block-coordinate update can be solved efficiently using Newton's method and further improved using an adaptive bisection method, solving these updates with a quadratic convergence rate. Our benchmarks show that our package adelie performs 3 to 10 times faster than the next fastest package on a wide array of both simulated and real datasets. Moreover, we demonstrate that our package is a competitive lasso solver as well, matching the performance of the popular lasso package glmnet.