{"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/fast-and-scalable-lasso-via-stochastic-frank","title":"Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee","arxiv_id":"1510.07169","date":"2015-10-24","proceeding":null,"authors":["Emanuele Frandi","Ricardo Nanculef","Stefano Lodi","Claudio Sartori","Johan A. K. Suykens"],"abstract":"Frank-Wolfe (FW) algorithms have been often proposed over the last few years\nas efficient solvers for a variety of optimization problems arising in the\nfield of Machine Learning. The ability to work with cheap projection-free\niterations and the incremental nature of the method make FW a very effective\nchoice for many large-scale problems where computing a sparse model is\ndesirable.\n  In this paper, we present a high-performance implementation of the FW method\ntailored to solve large-scale Lasso regression problems, based on a randomized\niteration, and prove that the convergence guarantees of the standard FW method\nare preserved in the stochastic setting. We show experimentally that our\nalgorithm outperforms several existing state of the art methods, including the\nCoordinate Descent algorithm by Friedman et al. (one of the fastest known Lasso\nsolvers), on several benchmark datasets with a very large number of features,\nwithout sacrificing the accuracy of the model. Our results illustrate that the\nalgorithm is able to generate the complete regularization path on problems of\nsize up to four million variables in less than one minute.","url_abs":"http://arxiv.org/abs/1510.07169v1","url_pdf":"http://arxiv.org/pdf/1510.07169v1.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":"fast-and-scalable-lasso-via-stochastic-frank","repo_url":"https://github.com/efrandi/FW-Lasso","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"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}