{"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-fast-randomized-algorithm-for-computing-the","title":"A Fast Randomized Algorithm for Computing an Approximate Null Space","arxiv_id":"2206.00975","date":"2022-06-02","proceeding":null,"authors":["Yuji Nakatsukasa","Taejun Park"],"abstract":"Randomized algorithms in numerical linear algebra can be fast, scalable and robust. This paper examines the effect of sketching on the right singular vectors corresponding to the smallest singular values of a tall-skinny matrix. We analyze a fast algorithm by Gilbert, Park and Wakin for finding the trailing right singular vectors using randomization by examining the quality of the solution using multiplicative perturbation theory. For an $m\\times n$ ($m\\geq n$) matrix, the algorithm runs with complexity $O(mn\\log n +n^3)$ which is faster than the standard $O(mn^2)$ methods. In applications, numerical experiments show great speedups including a $30\\times$ speedup for the AAA algorithm and $10\\times$ speedup for the total least squares problem.","url_abs":"https://arxiv.org/abs/2206.00975v3","url_pdf":"https://arxiv.org/pdf/2206.00975v3.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":"a-fast-randomized-algorithm-for-computing-the","repo_url":"https://github.com/tpark4466/fastapproxnullspace","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","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}