{"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-practical-guide-to-randomized-matrix","title":"A Practical Guide to Randomized Matrix Computations with MATLAB Implementations","arxiv_id":"1505.07570","date":"2015-05-28","proceeding":null,"authors":["Shusen Wang"],"abstract":"Matrix operations such as matrix inversion, eigenvalue decomposition,\nsingular value decomposition are ubiquitous in real-world applications.\nUnfortunately, many of these matrix operations so time and memory expensive\nthat they are prohibitive when the scale of data is large. In real-world\napplications, since the data themselves are noisy, machine-precision matrix\noperations are not necessary at all, and one can sacrifice a reasonable amount\nof accuracy for computational efficiency.\n  In recent years, a bunch of randomized algorithms have been devised to make\nmatrix computations more scalable. Mahoney (2011) and Woodruff (2014) have\nwritten excellent but very technical reviews of the randomized algorithms.\nDifferently, the focus of this manuscript is on intuition, algorithm\nderivation, and implementation. This manuscript should be accessible to people\nwith knowledge in elementary matrix algebra but unfamiliar with randomized\nmatrix computations. The algorithms introduced in this manuscript are all\nsummarized in a user-friendly way, and they can be implemented in lines of\nMATLAB code. The readers can easily follow the implementations even if they do\nnot understand the maths and algorithms.","url_abs":"http://arxiv.org/abs/1505.07570v6","url_pdf":"http://arxiv.org/pdf/1505.07570v6.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-practical-guide-to-randomized-matrix","repo_url":"https://github.com/wangshusen/RandMatrixMatlab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"}],"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}