On efficiency of parallel algorithms for global optimization of functions of several variables

We consider the problem of constructing efficient parallel algorithms for global optimization. The results are of given the qualitative analysis of the possibility of overcoming the exponential growth of global optimization problems for functions of general form, using the commonly used algorithmic techniques to accelerate convergence (Lipschitz conditions, reduction, local technique). Shown that the construction of efficient algorithms for global optimization, for the dimensions of 100 or more variables, is possible for specific problems, taking into account the specific characteristics of the function being optimized. The class of "good" functions is shown, as an example.