A Nonmonotonic Trust Region Technique for Nonlinear Constrained Optimization

Abstract

In this paper, a nonmonotonic trust region method for optimization problems with equality constraints is proposed by introducing a nonsmooth merit function and adopting a correction step. It is proved that all accumulation points of the iterates generated by the proposed algorithm are Kuhn-Tucker points and that the algorithm is $q$-superlinearly convergent.