{"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/the-grobnerwalk-jl-package-for-oscar","title":"The GroebnerWalk.jl package for OSCAR","arxiv_id":"2503.09254","date":"2025-03-12","proceeding":null,"authors":["Kamillo Ferry","Francesco Nowell"],"abstract":"Computing Gr\\\"obner bases is known to have a very high upper bound on computation time with respect to input length. Due to the connection between polyhedral geometry and Gr\\\"obner bases through the Gr\\\"obner fan, one can attempt an incremental approach to compute Gr\\\"obner bases. First computing a Gr\\\"obner basis with respect to an `easy' term order and transforming that result to a Gr\\\"obner basis with respect to the desired term order by using information about this polyhedral fan is done by a family of algorithms termed as Gr\\\"obner walk. We implemented two variants of the Gr\\\"obner walk in the computer algebra system OSCAR and compared their performance with classical Gr\\\"obner basis methods already found in OSCAR.","url_abs":"https://arxiv.org/abs/2503.09254v2","url_pdf":"https://arxiv.org/pdf/2503.09254v2.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"the-grobnerwalk-jl-package-for-oscar","repo_url":"https://github.com/ooinaruhugh/groebnerwalk.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}