Convergence of a projected gradient method with trust region for nonlinear constrained optimization†

Abstract

In this paper we describe a projected gradient algorithm with trust region, introducing a nondifferentiable merit function for solving nonlinear constrained optimization problems. We show that this method is globally convergent even if conditions are weak. It is also proved that, when the strict complementarity condition holds, the proposed algorithm can be solved by an equality constrained problem, allowing locally rate of superlinear convergence.