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• W3167796844 abstract "In this work, we develop efficient solvers for linear inverse problems based on randomized singular value decomposition (RSVD). This is achieved by combining RSVD with classical regularization methods, e.g., truncated singular value decomposition, Tikhonov regularization, and general Tikhonov regularization with a smoothness penalty. One distinct feature of the proposed approach is that it explicitly preserves the structure of the regularized solution in the sense that it always lies in the range of a certain adjoint operator. We provide error estimates between the approximation and the exact solution under canonical source condition, and interpret the approach in the lens of convex duality. Extensive numerical experiments are provided to illustrate the efficiency and accuracy of the approach." @default.
• W3167796844 created "2021-06-22" @default.
• W3167796844 creator A5004826461 @default.
• W3167796844 creator A5039605463 @default.
• W3167796844 date "2020-01-01" @default.
• W3167796844 modified "2023-09-30" @default.
• W3167796844 title "Regularized Linear Inversion with Randomized Singular Value Decomposition" @default.
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• W3167796844 doi "https://doi.org/10.1007/978-3-030-48634-1_5" @default.
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