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Randomized Algorithms for Solving Singular Value Decomposition Problems with Matlab Toolbox
dc.contributor.advisor | Miedlar, Agnieszka | |
dc.contributor.author | Li, Xiaowen | |
dc.date.accessioned | 2024-07-11T01:11:42Z | |
dc.date.available | 2024-07-11T01:11:42Z | |
dc.date.issued | 2021-05-31 | |
dc.date.submitted | 2021 | |
dc.identifier.other | http://dissertations.umi.com/ku:17810 | |
dc.identifier.uri | https://hdl.handle.net/1808/35445 | |
dc.description.abstract | This thesis gives an overview of the state-of-the-art randomized linear algebra algorithms for singular value decomposition (SVD), including the presentation of existing pseudo-codes and theoretical error analysis. Our main focus is on presenting numerical experiments illustrating image restoration using various randomized singular value decomposition (RSVD) methods; theoretical error bounds, computed errors, and canonical angles analysis for these RSVD algorithms.This thesis also comes with a newly developed Matlab toolbox that contains implementations and test examples for some of the state-of-the-art randomized numerical linear algebra algorithms. | |
dc.format.extent | 53 pages | |
dc.language.iso | en | |
dc.publisher | University of Kansas | |
dc.rights | Copyright held by the author. | |
dc.subject | Mathematics | |
dc.subject | Numerical Linear Algebra | |
dc.subject | Randomized Algorithms | |
dc.subject | Randomized Numerical Linear Algebra | |
dc.subject | Singular Value Decomposition | |
dc.title | Randomized Algorithms for Solving Singular Value Decomposition Problems with Matlab Toolbox | |
dc.type | Thesis | |
dc.contributor.cmtemember | Xu, Hongguo | |
dc.contributor.cmtemember | Cazeaux, Paul | |
dc.thesis.degreeDiscipline | Mathematics | |
dc.thesis.degreeLevel | M.A. | |
dc.identifier.orcid | 0000-0002-8942-6109 |
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