Least squares problems min(x) parallel to b - Ax parallel to(2) where the matrix A is an element of C-mXn (m >= n) has some particular structure arise frequently in applications. Polynomial data fitting is a well-known instance of problems that yield highly structured matrices, but many other examples exist. Very often, structured matrices have huge condition numbers kappa(2)(A) = parallel to A parallel to(2) parallel to A(dagger)parallel to(2) (A(dagger) is the Moore-Penrose pseudoinverse of A) and therefore standard algorithms fail to compute accurate minimum 2-norm solutions of least squares problems. In this work, we introduce a framework that allows us to compute minimum 2-norm solutions of many classes of structured least squares prob...
We address the problem of solving linear least-squares problems min——Ax−b—— when A is a sparse m-by-...
The a posteriori estimate of the errors in the numerical solution of ill-conditioned linear systems ...
AbstractFor a complex matrix equation AXB=C, we solve the following two problems: (1) the maximal an...
Least squares problems min(x) parallel to b - Ax parallel to(2) where the matrix A is an element of ...
We study the solution of the linear least-squares problem min_x \vert\vertb-Ax\vert\vert\₂ where the...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
International audienceUpdating a linear least squares solution can be critical for near real-time si...
Consider a full-rank weighted least-squares problem in which the weight matrix is highly ill-conditi...
. The linear least squares problem arises in many areas of sciences and engineerings. When the coef...
It is known that the computed least squares solution x of Ax=b, in the presence of the round-off err...
This paper is concerned with least squares problems when the least squares matrix A is near a matrix...
In this paper, we address the accuracy of the results for the overdetermined full rank linear least ...
In this paper, we study the problem of computing an LSP-decompositionof a matrix over a field. This ...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
AbstractSuppose that the linear system Ax=b is consistent and Ā=A+δA, b̄=b+δb are perturbed from A,...
We address the problem of solving linear least-squares problems min——Ax−b—— when A is a sparse m-by-...
The a posteriori estimate of the errors in the numerical solution of ill-conditioned linear systems ...
AbstractFor a complex matrix equation AXB=C, we solve the following two problems: (1) the maximal an...
Least squares problems min(x) parallel to b - Ax parallel to(2) where the matrix A is an element of ...
We study the solution of the linear least-squares problem min_x \vert\vertb-Ax\vert\vert\₂ where the...
This dissertation presents several fast and stable algorithms for both dense and sparse matrices bas...
International audienceUpdating a linear least squares solution can be critical for near real-time si...
Consider a full-rank weighted least-squares problem in which the weight matrix is highly ill-conditi...
. The linear least squares problem arises in many areas of sciences and engineerings. When the coef...
It is known that the computed least squares solution x of Ax=b, in the presence of the round-off err...
This paper is concerned with least squares problems when the least squares matrix A is near a matrix...
In this paper, we address the accuracy of the results for the overdetermined full rank linear least ...
In this paper, we study the problem of computing an LSP-decompositionof a matrix over a field. This ...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
AbstractSuppose that the linear system Ax=b is consistent and Ā=A+δA, b̄=b+δb are perturbed from A,...
We address the problem of solving linear least-squares problems min——Ax−b—— when A is a sparse m-by-...
The a posteriori estimate of the errors in the numerical solution of ill-conditioned linear systems ...
AbstractFor a complex matrix equation AXB=C, we solve the following two problems: (1) the maximal an...