AbstractRichardson's “extrapolation to the limit” idea is applied to the method of regularization for approximating the generalized inverse of a linear operator in Hilbert space. Uniform error bounds for successive extrapolates are derived for the case of bounded linear operators with closed range. For bounded linear operators with arbitrary range, and for densely defined closed linear operators, pointwise error bounds are derived, assuming certain “smoothness” conditions on the data
Inverse problems occur frequently in science and technology, whenever we need to infer causes from e...
Esta Tesis abarca el estudio de métodos de regularización para problemas inversos mal condicionados ...
We deal with the solution of a generic linear inverse problem in the Hilbert space setting. The exac...
. In this paper we present a method for solving problems Af = g by constructing an approximative inv...
For solving linear ill-posed problems with noisy data, regularization methods are required. In the p...
Based on the variable Hilbert scale interpolation inequality, bounds for the error of regularisation...
In this paper we investigate error estimates for the approximate solution of operator equations Af =...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
Abstract: We study linear inverse problems under the premise that the forward operator is not at han...
Abstract: We study linear inverse problems under the premise that the forward operator is not at han...
Abstract. This paper is concerned with a novel regularisation technique for solving linear ill-posed...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
The book collects and contributes new results on the theory and practice of ill-posed inverse proble...
This chapter studies the estimation of φ in linear inverse problems Tφ = r, where r is only observed...
For solving linear ill-posed problems with noisy data regularization methods are required. In the pr...
Inverse problems occur frequently in science and technology, whenever we need to infer causes from e...
Esta Tesis abarca el estudio de métodos de regularización para problemas inversos mal condicionados ...
We deal with the solution of a generic linear inverse problem in the Hilbert space setting. The exac...
. In this paper we present a method for solving problems Af = g by constructing an approximative inv...
For solving linear ill-posed problems with noisy data, regularization methods are required. In the p...
Based on the variable Hilbert scale interpolation inequality, bounds for the error of regularisation...
In this paper we investigate error estimates for the approximate solution of operator equations Af =...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
Abstract: We study linear inverse problems under the premise that the forward operator is not at han...
Abstract: We study linear inverse problems under the premise that the forward operator is not at han...
Abstract. This paper is concerned with a novel regularisation technique for solving linear ill-posed...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
The book collects and contributes new results on the theory and practice of ill-posed inverse proble...
This chapter studies the estimation of φ in linear inverse problems Tφ = r, where r is only observed...
For solving linear ill-posed problems with noisy data regularization methods are required. In the pr...
Inverse problems occur frequently in science and technology, whenever we need to infer causes from e...
Esta Tesis abarca el estudio de métodos de regularización para problemas inversos mal condicionados ...
We deal with the solution of a generic linear inverse problem in the Hilbert space setting. The exac...