In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the limited-memory BFGS (L-M-BFGS) update matrix is used in the trust-region sub-problem to improve the effectiveness of the algorithm for large-scale problems. The global convergence of the presented method is established under suitable conditions. The numeri-cal results of the test problems show that the method is competitive with the norm method
In this work we extend the Levenberg-Marquardt algorithm for approximating zeros of the nonlinear sy...
In this paper we present two new numerical methods for unconstrained large-scale optimization. These...
We present a new Newton-like method for large-scale unconstrained nonconvex minimization. And a new ...
In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the l...
In this paper, we consider the problem of solving nonlinear equations F (x) = 0, where F (x) from ! ...
In this paper, two new trust-region algorithms for the numerical solution of systems of nonlinear eq...
A new trust-region algorithm for solving the general nonlinear programming problem is introduced. In...
In this paper, a Cauchy point direction trust region algorithm is presented to solve nonlinear equat...
In this article, by means of an active set and limited memory strategy, we propose a trust-region me...
We present a matrix-free algorithm for the large-scale trust-region subproblem. Our algorithm relies...
Limited-memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
Limited memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
A trust-region-based BFGS method is proposed for solving symmetric nonlinear equations. In this give...
The aim of this text is to highlight recent advances of trust-region-based methods for nonlinear pro...
In this article, we propose a method for solving the trust-region subproblem when a limited-memory s...
In this work we extend the Levenberg-Marquardt algorithm for approximating zeros of the nonlinear sy...
In this paper we present two new numerical methods for unconstrained large-scale optimization. These...
We present a new Newton-like method for large-scale unconstrained nonconvex minimization. And a new ...
In this paper, a trust-region algorithm is proposed for large-scale nonlinear equations, where the l...
In this paper, we consider the problem of solving nonlinear equations F (x) = 0, where F (x) from ! ...
In this paper, two new trust-region algorithms for the numerical solution of systems of nonlinear eq...
A new trust-region algorithm for solving the general nonlinear programming problem is introduced. In...
In this paper, a Cauchy point direction trust region algorithm is presented to solve nonlinear equat...
In this article, by means of an active set and limited memory strategy, we propose a trust-region me...
We present a matrix-free algorithm for the large-scale trust-region subproblem. Our algorithm relies...
Limited-memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
Limited memory quasi-Newton methods and trust-region methods represent two efficient approaches used...
A trust-region-based BFGS method is proposed for solving symmetric nonlinear equations. In this give...
The aim of this text is to highlight recent advances of trust-region-based methods for nonlinear pro...
In this article, we propose a method for solving the trust-region subproblem when a limited-memory s...
In this work we extend the Levenberg-Marquardt algorithm for approximating zeros of the nonlinear sy...
In this paper we present two new numerical methods for unconstrained large-scale optimization. These...
We present a new Newton-like method for large-scale unconstrained nonconvex minimization. And a new ...