Introduced in 1993, the DIRECT global optimization algorithm provided a fresh approach to
minimizing a black-box function subject to lower and upper bounds on the variables. In …

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For several years, parts of the content of this textbook have been used in undergraduate
courses in the Department of Mathematics and in the Faculty of Economics at the University …

Direct-type global optimization algorithms often spend an excessive number of function
evaluations on problems with many local optima exploring suboptimal local minima, thereby …

In this work, we introduce DIRECTGO, a new MATLAB toolbox for derivative-free global
optimization. DIRECTGO collects various deterministic derivative-free DIRECT-type …

In this paper, we propose a modification of the well-known DBSCAN algorithm, which
recognizes clusters with various data densities in a given set of data points A={a^ i ∈ R^ n …

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 this paper we construct an efficient adaptive Mahalanobis k-means algorithm. In addition,
we propose a new efficient algorithm to search for a globally optimal partition obtained by …

In this paper, we consider the multiple ellipse detection problem based on data points
coming from a number of ellipses in the plane not known in advance. In so doing, data …

Global optimization algorithms discussed in the previous chapter, use the global estimate of
the Lipschitz constant L given a priori and do not take into account the local information …