On the rate of superlinear convergence of a class of variable metric methods

Summary This paper considers a class of variable metric methods for unconstrained minimization. Without requiring exact line searches each algorithm in this class converges globally and superlinearly on convex functions. Various results on the rate of the superlinear convergence are obtained.Dedicated to Professor Dr. H. Görtler on the occasion of his seventieth birthday