Convergence acceleration processes are known to be well conditioned for alternating sequences and ill conditioned for monotonic ones. The aim of this paper is to adapt the definition of conditioning and to give a link between this notion and the property of convergence acceleration. The cases of the
On the condition number of the antireflective transform
โ Scribed by Marco Donatelli; Martin Hanke
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 200 KB
- Volume
- 432
- Category
- Article
- ISSN
- 0024-3795
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โฆ Synopsis
Deconvolution problems with a finite observation window require appropriate models of the unknown signal in order to guarantee uniqueness of the solution. For this purpose it has recently been suggested to impose some kind of antireflectivity of the signal. With this constraint, the deconvolution problem can be solved with an appropriate modification of the fast sine transform, provided that the convolution kernel is symmetric. The corresponding transformation is called the antireflective transform. In this work we determine the condition number of the antireflective transform to first order, and use this to show that the so-called reblurring variant of Tikhonov regularization for deconvolution problems is a regularization method. Moreover, we establish upper bounds for the regularization error of the reblurring strategy that hold uniformly with respect to the size n of the algebraic system, even though the condition number of the antireflective transform grows with n. We briefly sketch how our results extend to higher space dimensions.
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