Rates of convergence for adaptive Newton methods

We consider Newton-type methods for constrained optimization problems in infinite-dimensional spaces, where at each iteration the first and second derivatives and the feasible set are approximated. The approximations can change at each iteration and conditions are given under which linear and superlinear rates of convergence of the iterates to the optimal point hold. Several applications are discussed.