{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-biglasso-package-a-memory-and-computation","title":"The biglasso Package: A Memory- and Computation-Efficient Solver for Lasso Model Fitting with Big Data in R","arxiv_id":"1701.05936","date":"2017-01-20","proceeding":null,"authors":["Yaohui Zeng","Patrick Breheny"],"abstract":"Penalized regression models such as the lasso have been extensively applied\nto analyzing high-dimensional data sets. However, due to memory limitations,\nexisting R packages like glmnet and ncvreg are not capable of fitting\nlasso-type models for ultrahigh-dimensional, multi-gigabyte data sets that are\nincreasingly seen in many areas such as genetics, genomics, biomedical imaging,\nand high-frequency finance. In this research, we implement an R package called\nbiglasso that tackles this challenge. biglasso utilizes memory-mapped files to\nstore the massive data on the disk, only reading data into memory when\nnecessary during model fitting, and is thus able to handle out-of-core\ncomputation seamlessly. Moreover, it's equipped with newly proposed, more\nefficient feature screening rules, which substantially accelerate the\ncomputation. Benchmarking experiments show that our biglasso package, as\ncompared to existing popular ones like glmnet, is much more memory- and\ncomputation-efficient. We further analyze a 31 GB real data set on a laptop\nwith only 16 GB RAM to demonstrate the out-of-core computation capability of\nbiglasso in analyzing massive data sets that cannot be accommodated by existing\nR packages.","url_abs":"http://arxiv.org/abs/1701.05936v2","url_pdf":"http://arxiv.org/pdf/1701.05936v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-biglasso-package-a-memory-and-computation","repo_url":"https://github.com/YaohuiZeng/biglasso","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-biglasso-package-a-memory-and-computation","repo_url":"https://github.com/conda-forge/r-biglasso-feedstock","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}