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Bibliographic Details
Main Author: Li, Xiaowen
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2402.17794
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author Li, Xiaowen
author_facet Li, Xiaowen
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2402_17794
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Randomized Algorithms for Solving Singular Value Decomposition Problems with Matlab Toolbox
Li, Xiaowen
Optimization and Control
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.
title Randomized Algorithms for Solving Singular Value Decomposition Problems with Matlab Toolbox
topic Optimization and Control
url https://arxiv.org/abs/2402.17794