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dc.contributor.authorO'Leary, Dianne P.en_US
dc.date.accessioned2004-05-31T22:56:36Z
dc.date.available2004-05-31T22:56:36Z
dc.date.created1999-03en_US
dc.date.issued1999-04-06en_US
dc.identifier.urihttp://hdl.handle.net/1903/1001
dc.description.abstractChoosing the regularization parameter for an ill-posed problem is an art based on good heuristics and prior knowledge of the noise in the observations. In this work we propose choosing the parameter, without a priori information, by approximately minimizing the distance between the true solution to the discrete problem and the family of regularized solutions. We demonstrate the usefulness of this approach for Tikhonov regularization and for an alternate family of solutions. Further, we prove convergence of the regularization parameter to zero as the standard deviation of the noise goes to zero. We also prove that the alternate family produces solutions closer to the true solution than the Tikhonov family when the noise is small enough. Also cross-referenced as UMIACS-TR-99-17en_US
dc.format.extent204417 bytes
dc.format.mimetypeapplication/postscript
dc.language.isoen_US
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-4004en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-99-17en_US
dc.titleNear-Optimal Parameters for Tikhonov and Other Regularization Methodsen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US


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