Global convergence of multidirectional algorithms for unconstrained optimization in normed spaces

Abstract

Global convergence theorems for a class of descent methods for unconstrained optimization problems in normed spaces, using multidirectional search, are proved. Exact and inexact search are considered and the results allow to define a globally convergent algoritm for an unconstrained optimal control problem which operates, at each step, on discrete approximations of the original continuous problem.