Show simple item record

dc.contributor.authorRojas, Marielba
Sorensen, Danny C.
dc.date.accessioned 2018-06-18T17:47:34Z
dc.date.available 2018-06-18T17:47:34Z
dc.date.issued 1999-12
dc.identifier.citation Rojas, Marielba and Sorensen, Danny C.. "A Trust-Region Approach to the Regularization of Large-Scale Discrete Ill-Posed Problems." (1999) https://hdl.handle.net/1911/101931.
dc.identifier.urihttps://hdl.handle.net/1911/101931
dc.description.abstract We consider the solution of large-scale least squares problems where the coefficient matrix comes from the discretization of an ill-posed operator and the right-hand size contains noise. Special techniques known as regularization methods are needed to treat these problems in order to control the effect of the noise on the solution. We pose the regularization problem as a trust-region subproblem and solve it by means of a recently developed method for the large-scale trust-region subproblem. We present numerical results on test problems, an inverse interpolation problem with real data, and a model seismic inversion problem with real data.
dc.format.extent 20 pp
dc.title A Trust-Region Approach to the Regularization of Large-Scale Discrete Ill-Posed Problems
dc.type Technical report
dc.date.note December 1999
dc.identifier.digital TR99-26
dc.type.dcmi Text


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record