A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent
<|reference_start|>A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent: 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.<|reference_end|>
@article{yang2024a,
    title={A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net
  Penalized Regression via Block-Coordinate Descent},
    author={James Yang, Trevor Hastie},
    journal={arXiv preprint arXiv:2405.08631},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08631},
    primaryClass={stat.CO cs.LG cs.MS cs.SE}
}