The total least squares (TLS) method is a successful approach for linear problems if both the right-hand side and the operator are contaminated by some noise. For ill-posed problems, a regularisation strategy has to be considered to stabilise the computed solution. Recently a double regularised TLS method was proposed within an infinite dimensional setup and it reconstructs both function and operator, reflected on the bilinear forms Our main focuses are on the design and the implementation of an algorithm with particular emphasis on alternating minimisation strategy, for solving not only the double regularised TLS problem, but a vast class of optimisation problems: on the minimisation of a bilinear functional of two variables.Peer reviewe
Many real-world applications are addressed through a linear least-squares problem formulation, whose...
AbstractIt is shown how structured and weighted total least squares and L2 approximation problems le...
The Tikhonov-Phillips method is widely used for regularizing ill-posed problems due to the simplicit...
The total least squares (TLS) method is a successful approach for linear problems if both the right-...
Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditione...
Discretizations of inverse problems lead to systems of linear equations with a highly ill-condition...
AbstractThe total least squares (TLS) method is a successful approach for linear problems when not o...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
Given a linear system Ax ≈ b over the real or complex field where both A and b are subject to noise,...
summary:The total least squares (TLS) and truncated TLS (T-TLS) methods are widely known linear data...
The total least squares (TLS) method is a successful approach for linear problems if both the matrix...
The total least squares (TLS) method is a successful approach for linear problems if both the system...
In the first part of the thesis we review basic knowledge of regularized least squares problems and ...
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Departme...
Recent advances in total least squares approaches for solving various errors-in-variables modeling p...
Many real-world applications are addressed through a linear least-squares problem formulation, whose...
AbstractIt is shown how structured and weighted total least squares and L2 approximation problems le...
The Tikhonov-Phillips method is widely used for regularizing ill-posed problems due to the simplicit...
The total least squares (TLS) method is a successful approach for linear problems if both the right-...
Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditione...
Discretizations of inverse problems lead to systems of linear equations with a highly ill-condition...
AbstractThe total least squares (TLS) method is a successful approach for linear problems when not o...
. Discretizations of inverse problems lead to systems of linear equations with a highly ill-conditio...
Given a linear system Ax ≈ b over the real or complex field where both A and b are subject to noise,...
summary:The total least squares (TLS) and truncated TLS (T-TLS) methods are widely known linear data...
The total least squares (TLS) method is a successful approach for linear problems if both the matrix...
The total least squares (TLS) method is a successful approach for linear problems if both the system...
In the first part of the thesis we review basic knowledge of regularized least squares problems and ...
Title: Regularization Techniques Based on the Least Squares Method Author: Marie Michenková Departme...
Recent advances in total least squares approaches for solving various errors-in-variables modeling p...
Many real-world applications are addressed through a linear least-squares problem formulation, whose...
AbstractIt is shown how structured and weighted total least squares and L2 approximation problems le...
The Tikhonov-Phillips method is widely used for regularizing ill-posed problems due to the simplicit...