The stable approximate solution of ill‐posed linear operator equations in Hilbert spaces requires regularization. Tight bounds for the noise‐free part of the regularization error are constitutive for bounding the overall error. Norm bounds of the noise‐free part which decrease to zero along with the regularization parameter are called profile functions and are the subject of our analysis. The interplay between properties of the regularization and certain smoothness properties of solution sets, which we shall describe in terms of sourcewise representations, is crucial for the decay of associated profile functions. On the one hand, we show that a given decay rate is possible only if the underlying true solution has appropriate smoothness. On ...
Abstract. For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive proce...
In this paper we discuss a relation between Learning Theory and Regularization of linear ill-posed i...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
The stable approximate solution of ill-posed linear operator equations in Hilbert spaces requires re...
In the analysis of ill-posed inverse problems the impact of solution smoothness on accuracy and conv...
The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear op...
The regularizing equations with a vector parameter of regularization are constructed for the linear ...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
Series : Applied and Numerical Harmonic AnalysisInternational audienceInverse problems and regulari...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
The stable solution of ill-posed non-linear operator equations in Banach space requires regularizati...
AbstractDue to the principle of regularization by restricting the number of degrees of freedom, trun...
Recent studies have shown how regularization may play an important role in linear system identificat...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
Abstract. This paper is concerned with a novel regularisation technique for solving linear ill-posed...
Abstract. For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive proce...
In this paper we discuss a relation between Learning Theory and Regularization of linear ill-posed i...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...
The stable approximate solution of ill-posed linear operator equations in Hilbert spaces requires re...
In the analysis of ill-posed inverse problems the impact of solution smoothness on accuracy and conv...
The paper is devoted to the analysis of ill-posed operator equations Ax = y with injective linear op...
The regularizing equations with a vector parameter of regularization are constructed for the linear ...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
Series : Applied and Numerical Harmonic AnalysisInternational audienceInverse problems and regulari...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
The stable solution of ill-posed non-linear operator equations in Banach space requires regularizati...
AbstractDue to the principle of regularization by restricting the number of degrees of freedom, trun...
Recent studies have shown how regularization may play an important role in linear system identificat...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
Abstract. This paper is concerned with a novel regularisation technique for solving linear ill-posed...
Abstract. For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive proce...
In this paper we discuss a relation between Learning Theory and Regularization of linear ill-posed i...
For solving linear ill-posed problems with noisy data regularization methods are required. In the p...