{"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/least-squares-auto-tuning","title":"Least Squares Auto-Tuning","arxiv_id":"1904.05460","date":"2019-04-10","proceeding":null,"authors":["Shane Barratt","Stephen Boyd"],"abstract":"Least squares is by far the simplest and most commonly applied computational\nmethod in many fields. In almost all applications, the least squares objective\nis rarely the true objective. We account for this discrepancy by parametrizing\nthe least squares problem and automatically adjusting these parameters using an\noptimization algorithm. We apply our method, which we call least squares\nauto-tuning, to data fitting.","url_abs":"http://arxiv.org/abs/1904.05460v1","url_pdf":"http://arxiv.org/pdf/1904.05460v1.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":"least-squares-auto-tuning","repo_url":"https://github.com/sbarratt/lsat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}