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Titel
L fitting for inverse problems with uniform noise
VerfasserClason, Christian
Erschienen in
ErschienenInst. of Physics
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
ISSN0266-5611
URNurn:nbn:at:at-ubg:3-242 Persistent Identifier (URN)
DOIdoi:10.1088/0266-5611/28/10/104007 
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L fitting for inverse problems with uniform noise [2.59 mb]
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Zusammenfassung (Englisch)

For inverse problems where the data are corrupted by uniform noise such as arising from quantization errors, the L norm is a more robust data-fitting term than the standard L2 norm. Well-posedness and regularization properties for linear inverse problems with L data fitting are shown, and the automatic choice of the regularization parameter is discussed. After introducing an equivalent reformulation of the problem and a MoreauYosida approximation, a superlinearly convergent semi-smooth Newton method becomes applicable for the numerical solution of L fitting problems. Numerical examples illustrate the performance of the proposed approach as well as the qualitative behavior of L fitting.

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