An implicit trust region algorithm for constrained optimization
Résumé
In this paper we study the convergence of sequential quadratic programming algorithms for the nonlinear programming problems. Assuming only that the direction is a stationary point of the current quadratic program we study the local convergence properties without strict complementarity. We obtain some global and superlinearly convergent algorithm. As a particular case we formulate an extension of Newton's method that is quadratically convergent to a point satisfying a strong sufficient second order condition.