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Regularization methods in Banach spaces

Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle inverse and ill-posed problems. Usually the mathematical model of an inverse problem consists of an operator equation of the first kind and often the associated forward operator acts between Hilbert s...

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Detalles Bibliográficos
Autores principales: Schuster, Thomas, Kaltenbacher, Barbara, Hofmann, Bernd, Kazimierski, Kamil S
Lenguaje:eng
Publicado: De Gruyter 2012
Materias:
Acceso en línea:http://cds.cern.ch/record/1614414
Descripción
Sumario:Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle inverse and ill-posed problems. Usually the mathematical model of an inverse problem consists of an operator equation of the first kind and often the associated forward operator acts between Hilbert spaces. However, for numerous problems the reasons for using a Hilbert space setting seem to be based rather on conventions than on an approprimate and realistic model choice, so often a Banach space setting would be closer to reality. Furthermore, sparsity constraints using general Lp-norms or the B