{"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-provable-algorithms-for-isotonic","title":"Fast, Provable Algorithms for Isotonic Regression in all $\\ell_{p}$-norms","arxiv_id":"1507.00710","date":"2015-07-02","proceeding":null,"authors":["Rasmus Kyng","Anup Rao","Sushant Sachdeva"],"abstract":"Given a directed acyclic graph $G,$ and a set of values $y$ on the vertices,\nthe Isotonic Regression of $y$ is a vector $x$ that respects the partial order\ndescribed by $G,$ and minimizes $||x-y||,$ for a specified norm. This paper\ngives improved algorithms for computing the Isotonic Regression for all\nweighted $\\ell_{p}$-norms with rigorous performance guarantees. Our algorithms\nare quite practical, and their variants can be implemented to run fast in\npractice.","url_abs":"http://arxiv.org/abs/1507.00710v2","url_pdf":"http://arxiv.org/pdf/1507.00710v2.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-provable-algorithms-for-isotonic","repo_url":"https://github.com/sachdevasushant/Isotonic","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"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}