Constrained dogleg methods for nonlinear systems with simple bounds

We focus on the numerical solution of medium scale bound-constrained systems of nonlinear equations. In this context, we consider an affine-scaling trust region approach that allows a great flexibility in choosing the scaling matrix used to handle the bounds. The method is based on a dogleg procedure tailored for constrained problems and so, it is named Constrained Dogleg method. It generates only strictly feasible iterates. Global and locally fast convergence is ensured under standard assumptions. The method has been implemented in the Matlab solver CoDoSol that supports several diagonal scalings in both spherical and elliptical trust region frameworks. We give a brief account of CoDoSol and report on the computational experience performed on a number of representative test problem

Solving Nonlinear Systems Of Equations By Means Of Quasi-Newton Methods With A Nonmonotone Strategy

A nonmonotone strategy for solving nonlinear systems of equations is introduced. The idea consists of combining efficient local methods with an algorithm that reduces monotonically the squared norm of the system in a proper way. The local methods used are Newton's method and two quasiNewton algorithms. Global iterations are based on recently introduced boxconstrained minimization algorithms. We present numerical experiments. 1 INTRODUCTION Given F : IR n ! IR n ; F = (f 1 ; : : : ; f n ) T , our aim is to find solutions of F (x) = 0: (1) We assume that F is well defined and has continuous partial derivatives on an open set of IR n . J(x) denotes the Jacobian matrix of partial derivatives of F (x). We are mostly interested in problems where n is large and J(x) is structurally sparse. This means that most entries of J(x) are zero for all x in the domain of F . The package Nightingale has been developed at the Department of Applied Mathematics of the University of Campinas for..