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Regularization in Banach spaces — convergence rates by approximative source conditions
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T. Hein
Published/Copyright:
February 4, 2009
Abstract
In this paper we deal with convergence rates for a Tikhonov regularization approach for linear and nonlinear ill-posed problems in Banach spaces. Here, we deal with so-called distance functions which quantify the violation of a given reference source condition. With the aid of these functions we present error bounds and convergence rates for regularized solutions of linear and nonlinear problems when the reference source condition is violated. Introducing this topic for linear problems we extend the theory also to nonlinear problems. Finally an a-posteriori choice of the regularization parameter is suggested yielding the optimal convergence rate.
Received: 2008-07-25
Published Online: 2009-02-04
Published in Print: 2009-February
© de Gruyter 2009
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Keywords for this article
Ill-posed problem;
regularization;
Banach space;
distance function;
convergence rates
Articles in the same Issue
- Minisymposium — Recent progress in regularization theory
- Recent results on the quasi-optimality principle
- An iterative thresholding-like algorithm for inverse problems with sparsity constraints in Banach space
- Regularization in Banach spaces — convergence rates by approximative source conditions
- On a parameter identification problem in linear elasticity
- Modified Landweber iterations in a multilevel algorithm applied to inverse problems in piezoelectricity
- On the role of sparsity in inverse problems
- Optimal convergence rates for Tikhonov regularization in Besov scales
- An overview on convergence rates for Tikhonov regularization methods for non-linear operators
- Modulus of continuity and conditional stability for linear regularization schemes
- Acceleration of the generalized Landweber method in Banach spaces via sequential subspace optimization
