We introduce a method that aims to find the global minimum of a continuous nonconvex
function on a compact subset of R^ d. It is assumed that function evaluations are expensive …

In science, engineering and economics, decision problems are frequently modelled by
optimizing the value of a (primary) objective function under stated feasibility constraints. In …

This is a survey of the main achievements in the methodology and theory of stochastic
global optimization. It comprises two complimentary directions: global random search and …

Branch and bound techniques are the most commonly used for the efficient solution of
nonconvex global optimization problems. Branching usually refers to a successive …

Optimization is a very broad field of research with a wide spectrum of important applications.
Until the 1950s, optimization was understood as a single-objective optimization, ie, as the …

Publisher Summary This chapter focuses on global optimization. The problem of designing
algorithms that distinguish between the local optima and locate the best possible one is …

As no algoritlun can solve a general, smooth global optimization problem with certainty in
finite time, stochastic methods are of eminent importance in global optimization. In this …

Bayesian decision theory is known to provide an effective framework for the practical
solution of discrete and nonconvex optimization problems. This book is the first to …

SYNOPTIC ABSTRACT The most efficient methods for finding the global minimum of an
objective function (not necessarily convex) are those that embody stochastic elements. In …

The implementation of global optimization algorithms, using the arithmetic of infinity, is
considered. A relatively simple version of implementation is proposed for the algorithms that …