Add to ORKGThe best least squares fitL A to a matrixA in a spaceL can be useful to improve the rate of convergence of the conjugate gradient method in solving systems Ax = b as well as to define low complexity quasi-Newton algorithms in unconstrained minimization. This is shown in the present paper with new important applications and ideas. Moreover, some theoretical results on the representation and on the computation of L A are investigated
Adaptive Least Squares Matching (ALSM) is a powerful technique for precisely locating objects in dig...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
A new code for solving the unconstrained least squares problem is given, in which a Quasi-NEWTON app...
The best least squares fit L_A to a matrix A in a space L can be useful to improve the rate of conve...
AbstractLet A ε ℛm × n(with m ⩾ n and rank (A) = n) and b ε ℛm × 1 be given. Assume that an approxim...
The Method of Least Squares is a procedure to determine the best fit line to data; the proof uses si...
AbstractConjugate gradient type methods are discussed for unsymmetric and inconsistent system of equ...
We derive a conjugate-gradient type algorithm to produce approximate least-squares (LS) solutions fo...
We present a new quasi-Newton method that can solve systems of equations of which no information is ...
summary:In this contribution, we propose a new hybrid method for minimization of nonlinear least squ...
Abstract. This paper deals with the problem of finding the minimum norm least-squares solution of a ...
Abstract. Iterative methods are often suitable for solving least-squares problems min kAx, bk2, wher...
In this contribution, we propose a new hybrid method for minimization of nonlinear least squares. Th...
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
AbstractThe quasi-Newton family of algorithms for minimizing functions and solving systems of nonlin...
Adaptive Least Squares Matching (ALSM) is a powerful technique for precisely locating objects in dig...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
A new code for solving the unconstrained least squares problem is given, in which a Quasi-NEWTON app...
The best least squares fit L_A to a matrix A in a space L can be useful to improve the rate of conve...
AbstractLet A ε ℛm × n(with m ⩾ n and rank (A) = n) and b ε ℛm × 1 be given. Assume that an approxim...
The Method of Least Squares is a procedure to determine the best fit line to data; the proof uses si...
AbstractConjugate gradient type methods are discussed for unsymmetric and inconsistent system of equ...
We derive a conjugate-gradient type algorithm to produce approximate least-squares (LS) solutions fo...
We present a new quasi-Newton method that can solve systems of equations of which no information is ...
summary:In this contribution, we propose a new hybrid method for minimization of nonlinear least squ...
Abstract. This paper deals with the problem of finding the minimum norm least-squares solution of a ...
Abstract. Iterative methods are often suitable for solving least-squares problems min kAx, bk2, wher...
In this contribution, we propose a new hybrid method for minimization of nonlinear least squares. Th...
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
AbstractThe quasi-Newton family of algorithms for minimizing functions and solving systems of nonlin...
Adaptive Least Squares Matching (ALSM) is a powerful technique for precisely locating objects in dig...
This thesis is focused on using low rank matrices in numerical mathematics. We introduce conjugate g...
A new code for solving the unconstrained least squares problem is given, in which a Quasi-NEWTON app...