Regularization of linear ill-posed problems involving multiplication operators
P. Mathé,M. T Nair,Bernd Hofmann +2 more
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In this article, the authors studied regularization of ill-posed equations involving multiplication operators when the multiplier function is positive almost everywhere and zero is an accumulation point of the range of this function.Abstract:
We study regularization of ill-posed equations involving multiplication operators when the multiplier function is positive almost everywhere and zero is an accumulation point of the range of this f...read more
Citations
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A fractional Landweber iterative regularization method for stable analytic continuation
TL;DR: In this paper, the authors considered the problem of analytic continuation of the analytic function on a strip domain and proposed the fraction Landweber iterative regularization method to deal with this problem.
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Regularization of ill-posed problems involving constant-coefficient pseudo-differential operators
TL;DR: In this paper , the wavelet regularization for ill-posed problems involving linear constant-coefficient pseudo-differential operators is studied and shown to achieve order-optimal rates of convergence for the a priori and the a posteriori choice rules.
References
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Interpolation of operators
C. Bennett,M Sharpley +1 more
TL;DR: In this article, the classical interpolation theorem is extended to the Banach Function Spaces, and the K-Method is used to find a Banach function space with a constant number of operators.
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Extrapolation, Interpolation, and Smoothing of Stationary Time Series: With Engineering Applications
TL;DR: Extrapolation interpolation and smoothing of stationary, stationary tones interference cancellation using adaptive and stationary time series financial definition of stationary.
Regularization Of Inverse Problems
TL;DR: The regularization of inverse problems is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can download it instantly.
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Variational Methods in Imaging
TL;DR: This book bridges the gap between regularization theory in image analysis and in inverse problems and introduces variational methods with motivation from the deterministic, geometric, and stochastic point of view.
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A convergence rates result for Tikhonov regularization in Banach spaces with non-smooth operators
TL;DR: In this article, the authors show that violations of the smoothness assumptions of the operator do not necessarily affect the convergence rate negatively, and they take this observation and weaken the smoothing assumptions on the operator and prove a novel convergence rate result.
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