Globally convergent methods for n-dimensional multiextremal optimization

A general class of n-dimensional direct (derivative-free) optimization procedures is introduced for solving multiextremal mathematical programming problems. For the case of minimizing a Lipschitz-continuous objective function on an n-dimensional interval, sufficient global convergence conditions are formulated and an efficiency estimate is given. Finally, some numerical aspects of the presented theoretical framework are summarized.