Add to ORKGTitle: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Department: Department of Numerical Mathematics Supervisor: RNDr. Iveta Hnětynková, Ph.D. Abstract: In this thesis we consider a linear inverse problem Ax ≈ b, where A is a linear operator with smoothing property and b represents an observation vector polluted by unknown noise. It was shown in [Hnětynková, Plešinger, Strakoš, 2009] that high-frequency noise reveals during the Golub-Kahan iterative bidiagonalization in the left bidiagonalization vectors. We propose a method that identifies the iteration with maximal noise revealing and reduces a portion of high-frequency noise in the data by subtracting the corresponding (properly scaled) left bidiago...
Abstract. We consider nonlinear inverse problems described by operator equations F (a) = u. Here a i...
AbstractWe consider Tikhonov regularization of linear ill-posed problems with noisy data. The choice...
The regularization of ill-posed systems of equations is carried out by corrections of the data or th...
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Departme...
In this thesis we consider a linear inverse problem Ax ≈ b with a smoothing operator A and a right-h...
In this thesis we consider problems Ax b arising from the discretization of ill-posed problems, wher...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
This thesis is a contribution to the field of ill-posed inverse problems . During the last ten year...
International audienceIn this paper, we propose two algorithms to solve a large class of linear inve...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems w...
Many real-world applications are addressed through a linear least-squares problem formulation, whose...
For solving linear ill-posed problems with noisy data, regularization methods are required. In this ...
Randomized methods can be competitive for the solution of problems with a large matrix of low rank. ...
Abstract. We consider nonlinear inverse problems described by operator equations F (a) = u. Here a i...
AbstractWe consider Tikhonov regularization of linear ill-posed problems with noisy data. The choice...
The regularization of ill-posed systems of equations is carried out by corrections of the data or th...
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Departme...
In this thesis we consider a linear inverse problem Ax ≈ b with a smoothing operator A and a right-h...
In this thesis we consider problems Ax b arising from the discretization of ill-posed problems, wher...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
This thesis is a contribution to the field of ill-posed inverse problems . During the last ten year...
International audienceIn this paper, we propose two algorithms to solve a large class of linear inve...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
Abstract. Regularization of ill-posed problems is only possible if certain bounds on the data noise ...
Tikhonov regularization is one of the most popular approaches to solve discrete ill-posed problems w...
Many real-world applications are addressed through a linear least-squares problem formulation, whose...
For solving linear ill-posed problems with noisy data, regularization methods are required. In this ...
Randomized methods can be competitive for the solution of problems with a large matrix of low rank. ...
Abstract. We consider nonlinear inverse problems described by operator equations F (a) = u. Here a i...
AbstractWe consider Tikhonov regularization of linear ill-posed problems with noisy data. The choice...
The regularization of ill-posed systems of equations is carried out by corrections of the data or th...