The main result of this paper is the formulation of an explicit expression for the condition number of the truncated least squares solution of $Ax=b$. This expression is given in terms of the singular values of $A$ and the Fourier coefficients of $b$. The result is derived using the notion of the Fréchet derivative together with the product norm on the data $[A,b]$ and the 2-norm on the solution. Numerical experiments are given to confirm our results by comparing them to those obtained by means of a finite difference approach
In this paper we introduce a new algorithm to estimate the optimal regularization parameter in trunc...
Data insufficiency, poorly conditioned matrices and singularities in equations occur regularly in co...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
International audienceThe main result of this paper is the formulation of an explicit expression for...
The truncated singular value decomposition is a popular solution method for linear discrete ill-pose...
In this note, we analyze the influence of the regularization procedure applied to singular LS square...
Linear discrete ill-posed problems of small to medium size are commonly solved by first computing th...
We derive closed formulas for the condition number of a linear function of the total least squares s...
AbstractStraightforward solution of discrete ill-posed least-squares problems with error-contaminate...
Abstract. Truncated singular value decomposition (TSVD) is a popular method for solving linear discr...
Abstract. In this paper we are interested in computing linear least squares (LLS) condition numbers ...
Abstract. This paper is concerned with the computation of accurate approximate solutions of linear s...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
AbstractWe consider the least-squares problem minx∈Rn ‖Kx − y‖2, where K is ill-conditioned and y co...
AbstractThis paper describes a new numerical method for the solution of large linear discrete ill-po...
In this paper we introduce a new algorithm to estimate the optimal regularization parameter in trunc...
Data insufficiency, poorly conditioned matrices and singularities in equations occur regularly in co...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...
International audienceThe main result of this paper is the formulation of an explicit expression for...
The truncated singular value decomposition is a popular solution method for linear discrete ill-pose...
In this note, we analyze the influence of the regularization procedure applied to singular LS square...
Linear discrete ill-posed problems of small to medium size are commonly solved by first computing th...
We derive closed formulas for the condition number of a linear function of the total least squares s...
AbstractStraightforward solution of discrete ill-posed least-squares problems with error-contaminate...
Abstract. Truncated singular value decomposition (TSVD) is a popular method for solving linear discr...
Abstract. In this paper we are interested in computing linear least squares (LLS) condition numbers ...
Abstract. This paper is concerned with the computation of accurate approximate solutions of linear s...
Straightforward solution of discrete ill-posed least-squares problems with error-contaminated data d...
AbstractWe consider the least-squares problem minx∈Rn ‖Kx − y‖2, where K is ill-conditioned and y co...
AbstractThis paper describes a new numerical method for the solution of large linear discrete ill-po...
In this paper we introduce a new algorithm to estimate the optimal regularization parameter in trunc...
Data insufficiency, poorly conditioned matrices and singularities in equations occur regularly in co...
The straightforward solution of discrete ill-posed linear systems of equations or least-squares prob...