To solve ill-posed problems Ax = f is used the Fakeev-Lardy regularization, using an adaptive discretization strategy. It is shown that for some classes of finitely smoothing operators proposed algorithm achieves the optimal order of accuracy and is more economical in the sense of amount of discrete information then standard method
Abstract. For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive proce...
There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlyin...
Problem of solving Fredholm integral equations of the first kind is a prototype of an ill-posed prob...
AbstractA new regularized projection method was developed for numerically solving ill-posed equation...
AbstractWe discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips r...
In this paper we deal with regularization approaches for discretized linear ill‐posed problems in Hi...
It is well known that projection schemes for certain linear ill-posed problems A퓍 = y can be regular...
Optimization of Projection Methods for Solving ill-posed Problems. In this paper we propose a modifi...
AbstractIn this paper, we consider a finite-dimensional approximation scheme combined with Tikhonov ...
In the article the authors developed two efficient algorithms for solving severely ill-posed problem...
Numerical solution of ill-posed problems is often accomplished by discretization (projection onto a...
Tikhonov regularization is one of the most popular methods for computing approximate solutions of l...
AbstractRegularized approximations to the solutions of ill-posed problems typically vary from over-s...
In the present paper for a stable solution of severely ill-posed problems with perturbed input data,...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
Abstract. For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive proce...
There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlyin...
Problem of solving Fredholm integral equations of the first kind is a prototype of an ill-posed prob...
AbstractA new regularized projection method was developed for numerically solving ill-posed equation...
AbstractWe discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips r...
In this paper we deal with regularization approaches for discretized linear ill‐posed problems in Hi...
It is well known that projection schemes for certain linear ill-posed problems A퓍 = y can be regular...
Optimization of Projection Methods for Solving ill-posed Problems. In this paper we propose a modifi...
AbstractIn this paper, we consider a finite-dimensional approximation scheme combined with Tikhonov ...
In the article the authors developed two efficient algorithms for solving severely ill-posed problem...
Numerical solution of ill-posed problems is often accomplished by discretization (projection onto a...
Tikhonov regularization is one of the most popular methods for computing approximate solutions of l...
AbstractRegularized approximations to the solutions of ill-posed problems typically vary from over-s...
In the present paper for a stable solution of severely ill-posed problems with perturbed input data,...
We study the efficiency of the approximate solution of ill-posed problems, based on discretized obse...
Abstract. For linear statistical ill-posed problems in Hilbert spaces we introduce an adaptive proce...
There are various ways to regularize ill-posed operator equations in Hilbert space. If the underlyin...
Problem of solving Fredholm integral equations of the first kind is a prototype of an ill-posed prob...