Abstract. In a recent paper, Dax has given numerical evidence of the advantages of using a modified fixed precision iterative refinement (updated residual) instead of the classical one (recom-puted residual) when a linear least squares problem is solved via the corrected seminormal equations of first kind with an accurate computation of the residual in mind. In this note we answer in the affirmative the natural question of whether Dax’s result remains valid when an accurate computa-tion of the minimum 2-norm solution of a linear underdetermined system is to be obtained via the corrected seminormal equations of second kind. Key words. fixed precision iterative refinement, linear least squares, minimum norm solution, linear underdetermined sy...
We propose a general algorithm for solving a $n\times n$ nonsingular linear system $Ax = b$ based on...
A general analysis of the condit4on of the linear least squares problem is given. The influence of r...
When a physical system is modeled by a nonlinear function, the unknown parameters can be estimated b...
The minimal 2-norm solution to an underdetermined system $Ax = b$ of full rank can be computed using...
Iterative refinement is a long-standing technique for improving the accuracy of a computed solution ...
Iterative refinement is a well-known technique for improving the quality of an approximate solution ...
We present the design and testing of an algorithm for iterative refinement of the solution of linear...
Many conjugate gradient-like methods for solving linear systems $Ax=b$ use recursion formulas for up...
Consider an over-determined linear system A'x = b and an under-determined linear system By = c. Give...
AbstractAn algorithm for computing solutions of overdetermined systems of linear equations in n real...
Abstract. An iterative method for solving a linear system Ax b produces iterates {xk with associated...
This paper is concerned with the solution of underdetermined linear systems of equations with a very...
AbstractWe develop successive overrelaxation (SOR) methods for finding the least squares solution of...
The problem of obtaining a minimum L 1e solution of an underdetermined system of consistent linear e...
Abstract. A linear program has a unique least 2-norm solution provided that the linear program has a...
We propose a general algorithm for solving a $n\times n$ nonsingular linear system $Ax = b$ based on...
A general analysis of the condit4on of the linear least squares problem is given. The influence of r...
When a physical system is modeled by a nonlinear function, the unknown parameters can be estimated b...
The minimal 2-norm solution to an underdetermined system $Ax = b$ of full rank can be computed using...
Iterative refinement is a long-standing technique for improving the accuracy of a computed solution ...
Iterative refinement is a well-known technique for improving the quality of an approximate solution ...
We present the design and testing of an algorithm for iterative refinement of the solution of linear...
Many conjugate gradient-like methods for solving linear systems $Ax=b$ use recursion formulas for up...
Consider an over-determined linear system A'x = b and an under-determined linear system By = c. Give...
AbstractAn algorithm for computing solutions of overdetermined systems of linear equations in n real...
Abstract. An iterative method for solving a linear system Ax b produces iterates {xk with associated...
This paper is concerned with the solution of underdetermined linear systems of equations with a very...
AbstractWe develop successive overrelaxation (SOR) methods for finding the least squares solution of...
The problem of obtaining a minimum L 1e solution of an underdetermined system of consistent linear e...
Abstract. A linear program has a unique least 2-norm solution provided that the linear program has a...
We propose a general algorithm for solving a $n\times n$ nonsingular linear system $Ax = b$ based on...
A general analysis of the condit4on of the linear least squares problem is given. The influence of r...
When a physical system is modeled by a nonlinear function, the unknown parameters can be estimated b...