Miedlar, AgnieszkaLi, Xiaowen2024-07-112024-07-112021-05-312021http://dissertations.umi.com/ku:17810https://hdl.handle.net/1808/35445This 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.53 pagesenCopyright held by the author.MathematicsNumerical Linear AlgebraRandomized AlgorithmsRandomized Numerical Linear AlgebraSingular Value DecompositionThesis0000-0002-8942-6109