{"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/large-scale-constrained-linear-regression","title":"Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning","arxiv_id":"1802.03337","date":"2018-02-09","proceeding":null,"authors":["Di Wang","Jinhui Xu"],"abstract":"In this paper, we revisit the large-scale constrained linear regression\nproblem and propose faster methods based on some recent developments in\nsketching and optimization. Our algorithms combine (accelerated) mini-batch SGD\nwith a new method called two-step preconditioning to achieve an approximate\nsolution with a time complexity lower than that of the state-of-the-art\ntechniques for the low precision case. Our idea can also be extended to the\nhigh precision case, which gives an alternative implementation to the Iterative\nHessian Sketch (IHS) method with significantly improved time complexity.\nExperiments on benchmark and synthetic datasets suggest that our methods indeed\noutperform existing ones considerably in both the low and high precision cases.","url_abs":"http://arxiv.org/abs/1802.03337v1","url_pdf":"http://arxiv.org/pdf/1802.03337v1.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":"large-scale-constrained-linear-regression","repo_url":"https://github.com/hiroyuki-kasai/SGDLibrary","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"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}