2005-11-11Buch DOI: 10.18452/2673
Global convergence of multidirectional algorithms for unconstrained optimization in normed spaces
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.
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