Hybrid and Iteratively Reweighted Regularization by Unbiased Predictive Risk and Weighted GCV for Projected Systems

@article{Renaut2017HybridAI,
  title={Hybrid and Iteratively Reweighted Regularization by Unbiased Predictive Risk and Weighted GCV for Projected Systems},
  author={Rosemary Anne Renaut and Saeed Vatankhah and Vahid Ebrahimzadeh Ardestani},
  journal={SIAM J. Sci. Comput.},
  year={2017},
  volume={39}
}
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 find a subspace approximation to the full problem. Determination of the regularization parameter using the method of unbiased predictive risk estimation is considered and contrasted with the generalized cross validation and discrepancy principle techniques. Examining the… Expand
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