In this work an iterative method to solve the nonlinear least squares problem is presented. The algorithm combines a secant method with a strategy of nonmonotone trust region. In order to dene the quadratic model, the Hessian matrix is chosen using a secant approach that takes advantage of the structure of the problem, and the radius of the trust region is updated following an adaptive technique. Moreover, convergence properties of this algorithm are proved. The numerical experimentation, in which several ways of choosing the Hessian matrix are compared, shows the effiency and robustness of the method.Sociedad Argentina de Informática e Investigación Operativ
In this study, we propose a trust-region-based procedure to solve unconstrained optimization problem...
An improved trust region method for unconstrained optimization Jun Liu In this paper, a new trust re...
summary:We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstr...
Based on a modified secant equation, we propose a scalar approximation of the Hessian to be used in ...
AbstractIn this paper, we combine the new trust region subproblem proposed in [1] with the nonmonoto...
summary:Trust region methods are a class of effective iterative schemes in numerical optimization. I...
In this paper, we present a new trust region method for unconstrained nonlinear programming in which...
The conditional, unconditional, or the exact maximum likelihood estimation and the least-squares est...
In this paper, we consider the problem of solving nonlinear equations F (x) = 0, where F (x) from ! ...
AbstractIn this paper we combine a reduced Hessian method with a mixed strategy using both trust reg...
The minimization of a quadratic function within an ellipsoidal trust region is an important subprobl...
AbstractIn this paper, we incorporate a nonmonotone technique with the new proposed adaptive trust r...
The minimization of a quadratic function within an ellipsoidal trust region is an important subprobl...
Includes bibliographical references (l. 37).This project presents a new approach to Quasi-Newton met...
AbstractThis paper concerns a nonmonotone line search technique and its application to the trust reg...
In this study, we propose a trust-region-based procedure to solve unconstrained optimization problem...
An improved trust region method for unconstrained optimization Jun Liu In this paper, a new trust re...
summary:We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstr...
Based on a modified secant equation, we propose a scalar approximation of the Hessian to be used in ...
AbstractIn this paper, we combine the new trust region subproblem proposed in [1] with the nonmonoto...
summary:Trust region methods are a class of effective iterative schemes in numerical optimization. I...
In this paper, we present a new trust region method for unconstrained nonlinear programming in which...
The conditional, unconditional, or the exact maximum likelihood estimation and the least-squares est...
In this paper, we consider the problem of solving nonlinear equations F (x) = 0, where F (x) from ! ...
AbstractIn this paper we combine a reduced Hessian method with a mixed strategy using both trust reg...
The minimization of a quadratic function within an ellipsoidal trust region is an important subprobl...
AbstractIn this paper, we incorporate a nonmonotone technique with the new proposed adaptive trust r...
The minimization of a quadratic function within an ellipsoidal trust region is an important subprobl...
Includes bibliographical references (l. 37).This project presents a new approach to Quasi-Newton met...
AbstractThis paper concerns a nonmonotone line search technique and its application to the trust reg...
In this study, we propose a trust-region-based procedure to solve unconstrained optimization problem...
An improved trust region method for unconstrained optimization Jun Liu In this paper, a new trust re...
summary:We propose a new and efficient nonmonotone adaptive trust region algorithm to solve unconstr...