Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems

dc.contributor.authorKilmer, Misha E.en_US
dc.contributor.authorO'Leary, Dianne P.en_US
dc.date.accessioned2004-05-31T22:52:56Z
dc.date.available2004-05-31T22:52:56Z
dc.date.created1998-10en_US
dc.date.issued1998-10-15en_US
dc.description.abstractNumerical solution of ill-posed problems is often accomplished by discretization (projection onto a finite dimensional subspace) followed by regularization. If the discrete problem has high dimension, though, typically we compute an approximate solution by projection onto an even smaller dimensional space, via iterative methods based on Krylov subspaces. In this work we present efficient algorithms that regularize after this second projection rather than before it. We prove some results on the approximate equivalence of this approach to other forms of regularization and we present numerical examples. (Also cross-referenced as UMIACS-TR-98-48)en_US
dc.format.extent254130 bytes
dc.format.mimetypeapplication/postscript
dc.identifier.urihttp://hdl.handle.net/1903/967
dc.language.isoen_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
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3937en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-98-48en_US
dc.titleChoosing Regularization Parameters in Iterative Methods for Ill-Posed Problemsen_US
dc.typeTechnical Reporten_US

Files

Original bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
CS-TR-3937.ps
Size:
248.17 KB
Format:
Postscript Files
Loading...
Thumbnail Image
Name:
CS-TR-3937.pdf
Size:
272.07 KB
Format:
Adobe Portable Document Format
Description:
Auto-generated copy of CS-TR-3937.ps