Multistart with early termination of descents

Multistart is a celebrated global optimization technique frequently applied in practice. In its pure form, multistart has low efficiency. However, the simplicity of multistart and multitude of possibilities of its generalization make it very attractive especially in high-dimensional problems where e.g. Lipschitzian and Bayesian algorithms are not applicable. We propose a version of multistart where most of the local descents are terminated very early; we will call it METOD as an abbreviation for multistart with early termination of descents. The performance of the proposed algorithm is demonstrated on randomly generated test functions with 100 variables and a modest number of local minimizers