{"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/a-sinkhorn-newton-method-for-entropic-optimal","title":"A Sinkhorn-Newton method for entropic optimal transport","arxiv_id":"1710.06635","date":"2017-10-18","proceeding":null,"authors":["Christoph Brauer","Christian Clason","Dirk Lorenz","Benedikt Wirth"],"abstract":"We consider the entropic regularization of discretized optimal transport and\npropose to solve its optimality conditions via a logarithmic Newton iteration.\nWe show a quadratic convergence rate and validate numerically that the method\ncompares favorably with the more commonly used Sinkhorn--Knopp algorithm for\nsmall regularization strength. We further investigate numerically the\nrobustness of the proposed method with respect to parameters such as the mesh\nsize of the discretization.","url_abs":"http://arxiv.org/abs/1710.06635v2","url_pdf":"http://arxiv.org/pdf/1710.06635v2.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":"a-sinkhorn-newton-method-for-entropic-optimal","repo_url":"https://github.com/dirloren/sinkhornnewton","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1710.06635","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}