114 citations · 220 across the 9 of their papers we have counts for
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math.NA2010★ 2 cited
Applying Lepskij-Balancing in Practice
Frank Bauer
In a stochastic noise setting the Lepskij balancing principle for choosing the regularization parameter in the regularization of inverse problems is depending on a parameter wh…
math.NA2010★ 2 cited
Parameter Choice by Fast Balancing
Frank Bauer
Choosing the regularization parameter for inverse problems is of major importance for the performance of the regularization method. We will introduce a fast version of the Lepskij…
math.NA2007★ 39 cited
Regularization independent of the noise level: an analysis of quasi-optimality
Frank Bauer, Markus Reiss
The quasi-optimality criterion chooses the regularization parameter in inverse problems without taking into account the noise level. This rule works remarkably well in practice, al…