{"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","title":"Distributed Coordinate Descent for Generalized Linear Models with Regularization","arxiv_id":"1611.02101","date":"2016-11-07","proceeding":null,"authors":["Ilya Trofimov","Alexander Genkin"],"abstract":"Generalized linear model with $L_1$ and $L_2$ regularization is a widely used\ntechnique for solving classification, class probability estimation and\nregression problems. With the numbers of both features and examples growing\nrapidly in the fields like text mining and clickstream data analysis\nparallelization and the use of cluster architectures becomes important. We\npresent a novel algorithm for fitting regularized generalized linear models in\nthe distributed environment. The algorithm splits data between nodes by\nfeatures, uses coordinate descent on each node and line search to merge results\nglobally. Convergence proof is provided. A modifications of the algorithm\naddresses slow node problem. For an important particular case of logistic\nregression we empirically compare our program with several state-of-the art\napproaches that rely on different algorithmic and data spitting methods.\nExperiments demonstrate that our approach is scalable and superior when\ntraining on large and sparse datasets.","url_abs":"http://arxiv.org/abs/1611.02101v2","url_pdf":"http://arxiv.org/pdf/1611.02101v2.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","repo_url":"https://github.com/IlyaTrofimov/dlr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}