Highly Influenced

@article{Hanke1996AGH, title={A General Heuristic for Choosing the Regularization Parameter in Ill-Posed Problems}, author={Martin Hanke and Toomas Raus}, journal={SIAM J. Scientific Computing}, year={1996}, volume={17}, pages={956-972} }

- Published 1996 in SIAM J. Scientific Computing
DOI:10.1137/0917062

For a variety of regularization methods, including Tikhonov regularization, Landweber iteration, v-method iteration, and the method of conjugate gradients, we develop and illustrate a heuristic for choosing an appropriate regularization parameter. Our choice requires no particular a priori knowledge, since the parameter is determined from computable information only. However, if an estimation for the noise level in the data is at hand, then this can be used as a justification. In contrast to… CONTINUE READING

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