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Tikhonov regularization for projected solutions of large-scale ill-posed problems is
considered. The Golub{Kahan iterative bidiagonalization is used to project the problem onto a
subspace and regularization then applied to

Tikhonov regularization for projected solutions of large-scale ill-posed problems is
considered. The Golub{Kahan iterative bidiagonalization is used to project the problem onto a
subspace and regularization then applied to nd a subspace approximation to the full problem.
Determination of the regularization, parameter for the projected problem by unbiased predictive risk
estimation, generalized cross validation, and discrepancy principle techniques is investigated. It is

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osemary A. Renaut, Saeed Vatankhah, and Vahid E. Ardestani (2017). Hybrid and Iteratively Reweighted Regularization by Unbiased Predictive Risk and Weighted GCV for Projected Systems, SIAM J. Sci. Comput. 39-2 (2017), pp. B221-B243. http://dx.doi.org/10.1137/15M1037925

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