In the thesis matrix approximation by randomized singular value decomposition is studied. In randomized singular value decomposition, a random sample of the image of a given matrix is used instead of the entire matrix. Upper bounds on the expected value of matrix approximation error are established by using two different methods. Probabilistic approximation error bound is established. Numerical examples are used to demonstrate the performance of algorithms.
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