Abstract
A class of parallel characteristical algorithms for global optimization of one-dimensional multiextremal functions is introduced. General convergence and efficiency conditions for the algorithms of the class introduced are established. A generalization for the multidimensional case is considered. Examples of parallel characteristical algorithms and numerical experiments are presented.
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