On convergence of "divide the best" global optimization algorithms

In this paper a new class of multidimensional global optimization algorithms (called "divide the best" algorithms) is proposed. The class unifies and generalizes the classes of the characteristic methods and the adaptive partition algorithms introduced by Grishagin and Pinter respectively....

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Bibliographic Details
Published in:	Optimization Vol. 44; no. 3; pp. 303 - 325
Main Author:	Sergeyev, Yaroslav D.
Format:	Journal Article
Language:	English
Published:	Gordon and Breach Science Publishers 01.01.1998
Subjects:	convergence Global optimization Mathematics Subject Classification 1991 numerical algorithms Primary: 90C30 Secondary: 49M37
ISSN:	0233-1934, 1029-4945
Online Access:	Get full text
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Summary:	In this paper a new class of multidimensional global optimization algorithms (called "divide the best" algorithms) is proposed. The class unifies and generalizes the classes of the characteristic methods and the adaptive partition algorithms introduced by Grishagin and Pinter respectively. The new scheme includes also some methods which do not fit either the characteristic or the adaptive partition families. A detailed convergence study is presented. A special attention is paid to cases where sufficient conditions of everywhere dense, local and global convergence are fulfilled only over subregions of the search domain.
ISSN:	0233-1934 1029-4945
DOI:	10.1080/02331939808844414