AbstractWe discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips regularization of discretized linear operator equations. Two rules turn out to be based entirely on data from the underlying regularization scheme. Among them, only the discrepancy principle allows us to search for the optimal regularization parameter from the easiest problem. This potential advantage cannot be achieved by the standard projection scheme. We present a modified scheme, in which the discretization level varies with the successive regularization parameters, which has the advantage, mentioned before
To solve ill-posed problems Ax = f is used the Fakeev-Lardy regularization, using an adaptive discre...
We present a discrepancy-based parameter choice and stopping rule for iterative algorithms performin...
AbstractIn this paper, we study the multi-parameter Tikhonov regularization method which adds multip...
AbstractWe discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips r...
The stable solution of ill-posed non-linear operator equations in Banach space requires regularizati...
Tikhonov regularization is one of the most popular methods for computing approximate solutions of l...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
To solve a linear ill-posed problem, a combination of the finite dimensional least squares projectio...
Multiplicative regularization solves a linear inverse problem by minimizing the product of the norm ...
It is shown that Tikhonov regularization for ill- posed operator equation \(Kx = y\) using a possib...
We address the classical issue of appropriate choice of the regularization and dis-cretization level...
Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems w...
summary:We give a derivation of an a-posteriori strategy for choosing the regularization parameter i...
We are concerned with a parameter choice strategy for the Tikhonov regularization \((\tilde{A}+\alph...
Choosing the regularization parameter for an ill-posed problem is an art based on good heuristics an...
To solve ill-posed problems Ax = f is used the Fakeev-Lardy regularization, using an adaptive discre...
We present a discrepancy-based parameter choice and stopping rule for iterative algorithms performin...
AbstractIn this paper, we study the multi-parameter Tikhonov regularization method which adds multip...
AbstractWe discuss adaptive strategies for choosing regularization parameters in Tikhonov–Phillips r...
The stable solution of ill-posed non-linear operator equations in Banach space requires regularizati...
Tikhonov regularization is one of the most popular methods for computing approximate solutions of l...
We study the application of Tikhonov regularization to ill-posed nonlinear operator equations. The o...
To solve a linear ill-posed problem, a combination of the finite dimensional least squares projectio...
Multiplicative regularization solves a linear inverse problem by minimizing the product of the norm ...
It is shown that Tikhonov regularization for ill- posed operator equation \(Kx = y\) using a possib...
We address the classical issue of appropriate choice of the regularization and dis-cretization level...
Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems w...
summary:We give a derivation of an a-posteriori strategy for choosing the regularization parameter i...
We are concerned with a parameter choice strategy for the Tikhonov regularization \((\tilde{A}+\alph...
Choosing the regularization parameter for an ill-posed problem is an art based on good heuristics an...
To solve ill-posed problems Ax = f is used the Fakeev-Lardy regularization, using an adaptive discre...
We present a discrepancy-based parameter choice and stopping rule for iterative algorithms performin...
AbstractIn this paper, we study the multi-parameter Tikhonov regularization method which adds multip...