Comparing RSVD and Krylov methods for linear inverse problems

In this work we address regularization parameter estimation for ill-posed linear inverse problems with an ℓ2 penalty. Regularization parameter selection is of utmost importance for all of inverse problems and estimating it generally relies on the experience of the practitioner. For regularization wi...

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Veröffentlicht in:	Computers & geosciences Jg. 137; S. 104427
Hauptverfasser:	Luiken, Nick, van Leeuwen, Tristan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.04.2020
Schlagworte:	algorithms computers geostatistics Krylov subspaces Model order reduction model validation Randomized singular value decomposition Regularization parameter spatial data Tikhonov regularization Model order reduction Randomized singular value decomposition Krylov subspaces Tikhonov regularization Regularization parameter
ISSN:	0098-3004, 1873-7803
Online-Zugang:	Volltext
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