Abstract. We discuss the solution of large-scale box-constrained linear least-squares problems by two recent affine-scaling methods: a cyclic Barzilai-Borwein strategy and an Inexact Newton-like method where a preconditioning technique allows for an efficient computation of the steps. A robust globally and fast locally convergent method based on the combination of the two procedures is presented along with extensive numerical results. 1. Introduction. W
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
A method for the solution of minimization problems with simple bounds is presented. Global convergen...
In this paper, we present a brief survey of methods for solving nonlinear least-squares problems. We...
We present an efficient algorithm for large-scale non-negative least-squares (NNLS). We solve NNLS b...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
We propose an iterative method that solves constrained linear least-squares problems by formulating ...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
Abstract. We propose an iterative method that solves constrained linear least-squares problems by fo...
Numerous scientific applications across a variety of fields depend on box-constrained convex optimiz...
Recently, we have presented a projected structured algorithm for solving constrained nonlinear least...
This paper extends prior work by the authors on solving nonlinear least squares unconstrained proble...
The well known Levenberg-Marquardt method is used extensively for solving nonlinear least-squares pr...
Constrained least squares estimation lies at the heart of many applications in fields as diverse as ...
The main contribution of this paper is presenting a flexible solution to the box-constrained least s...
Abstract. In this paper, a Gauss-Newton method is proposed for the solution of large-scale nonlinear...
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
A method for the solution of minimization problems with simple bounds is presented. Global convergen...
In this paper, we present a brief survey of methods for solving nonlinear least-squares problems. We...
We present an efficient algorithm for large-scale non-negative least-squares (NNLS). We solve NNLS b...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
We propose an iterative method that solves constrained linear least-squares problems by formulating ...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
Abstract. We propose an iterative method that solves constrained linear least-squares problems by fo...
Numerous scientific applications across a variety of fields depend on box-constrained convex optimiz...
Recently, we have presented a projected structured algorithm for solving constrained nonlinear least...
This paper extends prior work by the authors on solving nonlinear least squares unconstrained proble...
The well known Levenberg-Marquardt method is used extensively for solving nonlinear least-squares pr...
Constrained least squares estimation lies at the heart of many applications in fields as diverse as ...
The main contribution of this paper is presenting a flexible solution to the box-constrained least s...
Abstract. In this paper, a Gauss-Newton method is proposed for the solution of large-scale nonlinear...
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
A method for the solution of minimization problems with simple bounds is presented. Global convergen...
In this paper, we present a brief survey of methods for solving nonlinear least-squares problems. We...