Add to ORKGSimplified regularization using finite-dimensional approximations in the setting of Hilbert scales has been considered for obtaining stable approximate solutions to ill-posed oper-ator equations. The derived error estimates using an a priori and a posteriori choice of parameters in relation to the noise level are shown to be of optimal order with respect to certain natural assumptions on the ill posedness of the equation. The results are shown to be applicable to a wide class of spline approximations in the setting of Sobolev scales
The advent of the computer had forced the application of mathematics to all branches of human endeav...
An iterative regularization method in the setting of a finite dimen-sional subspace Xh of the real H...
The computation of an approximate solution of linear discrete illposed problems with contaminated da...
Simplified regularization using finite-dimensional approximations in the setting of Hilbert scales h...
Recently, Tautenhahn and Hämarik (1999) have considered a monotone rule as a parameter choice strate...
For solving linear ill-posed problems with noisy data, regularization methods are required. In the p...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized nois...
For solving linear ill-posed problems, regularization methods are required when the right-hand side ...
A class of regularization methods using unbounded regularizing operators is considered for obtaining...
AbstractWe investigate a general class of regularization methods for ill-posed linear operator equat...
It is well known that projection schemes for certain linear ill-posed problems $Ax = y$ can be regul...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
The problem of minimizing the difficulty of the inverse estimation of some unknown element x_0 from ...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
The advent of the computer had forced the application of mathematics to all branches of human endeav...
An iterative regularization method in the setting of a finite dimen-sional subspace Xh of the real H...
The computation of an approximate solution of linear discrete illposed problems with contaminated da...
Simplified regularization using finite-dimensional approximations in the setting of Hilbert scales h...
Recently, Tautenhahn and Hämarik (1999) have considered a monotone rule as a parameter choice strate...
For solving linear ill-posed problems with noisy data, regularization methods are required. In the p...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized nois...
For solving linear ill-posed problems, regularization methods are required when the right-hand side ...
A class of regularization methods using unbounded regularizing operators is considered for obtaining...
AbstractWe investigate a general class of regularization methods for ill-posed linear operator equat...
It is well known that projection schemes for certain linear ill-posed problems $Ax = y$ can be regul...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
The problem of minimizing the difficulty of the inverse estimation of some unknown element x_0 from ...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
The advent of the computer had forced the application of mathematics to all branches of human endeav...
An iterative regularization method in the setting of a finite dimen-sional subspace Xh of the real H...
The computation of an approximate solution of linear discrete illposed problems with contaminated da...