Many practical problems involve the search for the global extremum in the space of the
system parameters. The functions to be optimized are often highly multiextremal, black-box …

This paper deals with two kinds of the one-dimensional global optimization problem over a
closed finite interval:(i) the objective function f(x) satisfies the Lipschitz condition with a …

In many important design problems, some decisions should be made by finding the global
optimum of a multiextremal objective function subject to a set of constrains. Frequently …

In many practical decision-making problems it happens that functions involved in
optimization process are black-box with unknown analytical representations and hard to …

This paper is devoted to numerical global optimization algorithms applying several ideas to
reduce the problem dimension. Two approaches to the dimensionality reduction are …

In this paper, the problem of safe global maximization (it should not be confused with robust
optimization) of expensive noisy black-box functions satisfying the Lipschitz condition is …

In this paper, the global optimization problem miny∈ SF (y) with S=[a, b], a, b∈ RN, and F
(y) satisfying the Lipschitz condition, is considered. To deal with it four algorithms are …

There exist many applications where it is necessary to approximate numerically derivatives
of a function which is given by a computer procedure. In particular, all the fields of …

Many control problems involve the search for the global extremum in the space of states or
the parameters of the system under study, which leads to the necessity of using effective …

Lipschitz global optimization is an important research field with numerous applications in
engineering, electronics, machine learning, optimal decision making, etc. In many of these …