A Trust-Region Approach to the Regularization of Large-Scale Discrete Ill-Posed Problems

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dc.contributor.authorRojas, Marielba
dc.contributor.authorSorensen, Danny C.
dc.date.accessioned2018-06-18T17:47:34Z
dc.date.available2018-06-18T17:47:34Z
dc.date.issued1999-12
dc.date.noteDecember 1999
dc.description.abstractWe 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.extent20 pp
dc.identifier.citationRojas, Marielba and Sorensen, Danny C.. "A Trust-Region Approach to the Regularization of Large-Scale Discrete Ill-Posed Problems." (1999) <a href="https://hdl.handle.net/1911/101931">https://hdl.handle.net/1911/101931</a>.
dc.identifier.digitalTR99-26
dc.identifier.urihttps://hdl.handle.net/1911/101931
dc.language.isoeng
dc.titleA Trust-Region Approach to the Regularization of Large-Scale Discrete Ill-Posed Problems
dc.typeTechnical report
dc.type.dcmiText
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