{"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/distributed-coordinate-descent-for-l1","title":"Distributed Coordinate Descent for L1-regularized Logistic Regression","arxiv_id":"1411.6520","date":"2014-11-24","proceeding":null,"authors":["Ilya Trofimov","Alexander Genkin"],"abstract":"Solving logistic regression with L1-regularization in distributed settings is\nan important problem. This problem arises when training dataset is very large\nand cannot fit the memory of a single machine. We present d-GLMNET, a new\nalgorithm solving logistic regression with L1-regularization in the distributed\nsettings. We empirically show that it is superior over distributed online\nlearning via truncated gradient.","url_abs":"http://arxiv.org/abs/1411.6520v1","url_pdf":"http://arxiv.org/pdf/1411.6520v1.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":"distributed-coordinate-descent-for-l1","repo_url":"https://github.com/IlyaTrofimov/dlr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}