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Regularisation by Krylov methods
If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.
Inverse problems are ubiquitous in many areas of Science and Engineering and, once discretised, they lead to ill-conditioned linear systems, often of huge dimensions: regularisation consists in replacing the original system by a nearby problem with better numerical properties, in order to find a meaningful approximation of its solution. After briefly surveying some standard regularisation methods, both iterative (such as many Krylov methods) and direct (such as Tikhonov method), this talk will introduce the recent class of the Krylov-Tikhonov methods, which merge an iterative and a direct approach to regularisation. In particular, strategies for choosing the regularization parameter and the regularization matrix will be emphasised.
This talk is part of the Applied and Computational Analysis series.
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