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On convergence of “divide the best” global optimization algorithms. (English) Zbl 0986.90058

The author introduces a new class of multidimensional global optimization algorithms, the so-called divide the best algorithms. They unify and generalize the characteristic methods, introduced by Grishagin, and the adaptive partition algorithms, introduced by Pinter. A detailed convergence study is presented. The author gives a special attention to the case, when sufficient conditions of everywhere dense, local and global convergence are satisfied only over subregions of the search domain.
Reviewer: D.Nowack (Berlin)

MSC:

90C30 Nonlinear programming
49M37 Numerical methods based on nonlinear programming
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