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

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dc.contributor.authorRojas, Marielbaen_US
dc.date.accessioned2018-06-18T17:47:05Zen_US
dc.date.available2018-06-18T17:47:05Zen_US
dc.date.issued1998-05en_US
dc.date.noteMay 1998en_US
dc.descriptionThis work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/19422en_US
dc.description.abstractWe consider the problem of computing the solution of large-scale discrete ill-posed problems when there is noise in the data. These problems arise in important areas such as seismic inversion, medical imaging and signal processing. We pose the problem as a quadratically constrained least squares problem and develop a method for the solution of such problem. Our method does not require factorization of the coefficient matrix, it has very low storage requirements and handles the high degree of singularities arising in discrete ill-posed problems. We present numerical results on test problems and an application of the method to a practical problem with real data.en_US
dc.format.extent135 ppen_US
dc.identifier.citationRojas, Marielba. "A Large-Scale Trust-Region Approach to the Regularization of Discrete Ill-Posed Problems." (1998) <a href="https://hdl.handle.net/1911/101904">https://hdl.handle.net/1911/101904</a>.en_US
dc.identifier.digitalTR98-19en_US
dc.identifier.urihttps://hdl.handle.net/1911/101904en_US
dc.language.isoengen_US
dc.titleA Large-Scale Trust-Region Approach to the Regularization of Discrete Ill-Posed Problemsen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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