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

dc.contributor.authorRojas, Marielbaen_US
dc.contributor.authorSorensen, Danny C.en_US
dc.date.accessioned2018-06-18T17:47:34Zen_US
dc.date.available2018-06-18T17:47:34Zen_US
dc.date.issued1999-12en_US
dc.date.noteDecember 1999en_US
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.en_US
dc.format.extent20 ppen_US
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>.en_US
dc.identifier.digitalTR99-26en_US
dc.identifier.urihttps://hdl.handle.net/1911/101931en_US
dc.language.isoengen_US
dc.titleA Trust-Region Approach to the Regularization of Large-Scale Discrete Ill-Posed Problemsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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