A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914796114280448 |
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| author | Yang, James Hastie, Trevor |
| author_facet | Yang, James Hastie, Trevor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent Yang, James Hastie, Trevor Computation Machine Learning Mathematical Software Software Engineering 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. |
| title | A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent |
| topic | Computation Machine Learning Mathematical Software Software Engineering |
| url | https://arxiv.org/abs/2405.08631 |