A large-scale trust-region approach to the regularization of discrete ill-posed problems

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dc.contributor.advisorSorensen, Danny C.
dc.creatorRojas, Marielba
dc.date.accessioned2009-06-04T06:45:59Z
dc.date.available2009-06-04T06:45:59Z
dc.date.issued1999
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.
dc.format.extent123 p.en_US
dc.format.mimetypeapplication/pdf
dc.identifier.callnoTHESIS MATH.SCI. 1999 ROJAS
dc.identifier.citationRojas, Marielba. "A large-scale trust-region approach to the regularization of discrete ill-posed problems." (1999) Diss., Rice University. <a href="https://hdl.handle.net/1911/19422">https://hdl.handle.net/1911/19422</a>.
dc.identifier.urihttps://hdl.handle.net/1911/19422
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectMathematics
dc.subjectComputer science
dc.titleA large-scale trust-region approach to the regularization of discrete ill-posed problems
dc.typeThesis
dc.type.materialText
thesis.degree.departmentMathematical Sciences
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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