A New Trust Region Algorithm for Equality Constrained Optimization

Abstract

We present a new trust algorithm for solving nonlinear equality constrained optimization problems. At each iterate a change of variables is performed to improve the ability of the algorithm to follow the constraint level sets. The algorithm employs L2 penalty function for obtaining global convergence. Under certain assumptions we prove that this algorithm globally converges to a point satisfying the second order necessary optimally conditions; the local convergence rate is quadratic. Results of preliminary numerical experiments are presented.