AN ADAPTIVE TRUST REGION METHOD FOR EQUALITY CONSTRAINED OPTIMIZATION

In this paper, a trust region method for equality constrained optimizationbased on nondifferentiable exact penalty is proposed. In this algorithm, the trail stepis characterized by computation of its normal component being separated from compu-tation of its tangential component, i.e., only the tangential component of the trail stepis constrained by trust radius while the normal component and trail step itself have noconstraints. The other main characteristic of the algorithm is the decision of trust regionradius. Here, the decision of trust region radius uses the information of the gradient ofobjective function and reduced Hessian. However, Maratos effect will occur when we usethe nondifferentiable exact penalty function as the merit function. In order to obtain thesuperlinear convergence of the algorithm, we use the twice order correction technique. Be-cause of the speciality of the adaptive trust region method, we use twice order correctionwhen p=0 (the definition is as in Section 2) and this is diffe