ON A CLASS OF HYBRID METHODS FOR SMOOTH CONSTRAINED OPTIMIZATION

With reference to smooth nonlinearly constrained optimization problems, we consider combinations of locally superlinearly convergent methods with globally convergent ones. The aim of this paper is threefold: to give a survey on well-known as well as possibly unknown hybrid optimization methods, based on a special construction principle; to present a general convergence result for the class of hybrid algorithms; and to derive further methods for this class with new convergence properties.