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- W2899557605 abstract "LSQR and its mathematically equivalent CGLS have been popularly used over the decades for large-scale linear discrete ill-posed problems, where the iteration number $k$ plays the role of the regularization parameter. It has been long known that if the Ritz values in LSQR converge to the large singular values of $A$ in natural order until its semi-convergence then LSQR must have the same the regularization ability as the truncated singular value decomposition (TSVD) method and can compute a 2-norm filtering best possible regularized solution. However, hitherto there has been no definitive rigorous result on the approximation behavior of the Ritz values in the context of ill-posed problems. In this paper, for severely, moderately and mildly ill-posed problems, we give accurate solutions of the two closely related fundamental and highly challenging problems on the regularization of LSQR: (i) How accurate are the low rank approximations generated by Lanczos bidiagonalization? (ii) Whether or not the Ritz values involved in LSQR approximate the large singular values of $A$ in natural order? We also show how to judge the accuracy of low rank approximations reliably during computation without extra cost. Numerical experiments confirm our results." @default.
- W2899557605 created "2018-11-16" @default.
- W2899557605 creator A5075378610 @default.
- W2899557605 date "2020-03-31" @default.
- W2899557605 modified "2023-09-27" @default.
- W2899557605 title "The low rank approximations and Ritz values in LSQR for linear discrete ill-posed problem" @default.
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- W2899557605 doi "https://doi.org/10.1088/1361-6420/ab6f42" @default.
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