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Randomized Algorithms for Solving Singular Value Decomposition Problems with Matlab Toolbox
Li, Xiaowen
Li, Xiaowen
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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.
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2021-05-31
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University of Kansas
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822868_1.pdf
Adobe PDF, 6.12 MB
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Keywords
Mathematics, Numerical Linear Algebra, Randomized Algorithms, Randomized Numerical Linear Algebra, Singular Value Decomposition