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- W2045596450 abstract "The truncated singular value decomposition (SVD) method is useful for solving the standard-form regularization problem: $min ||{bf x}||_2 $ subject to $min ||A{bf x} - {bf b}||_2 $. This paper presents a modification of the truncated SVD method, which solves the more general problem: $min ||L{bf x}||_2 $ subject to $min ||A{bf x} - {bf b}||_2 $, where L is a general matrix with full row rank. The extra work, associated with the introduction of the matrix L, is dominated by a QR-factorization of a matrix with dimensions smaller than those of L. In order to determine the optimal solution, it is often necessary to compute a sequence of regularized solutions, and it is shown how this can be accomplished with little extra computational effort. Finally, the new method is illustrated with an example from helioseismology." @default.
- W2045596450 created "2016-06-24" @default.
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- W2045596450 date "1992-09-01" @default.
- W2045596450 modified "2023-10-16" @default.
- W2045596450 title "The Modified Truncated SVD Method for Regularization in General Form" @default.
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- W2045596450 doi "https://doi.org/10.1137/0913066" @default.
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