The term global optimization is used in several contexts. Most often we are interested in finding such a point (or points) in many-dimensional search space at which the objective function's value is optimal, i.e. maximal or minimal. Sometimes, however, we are also interested in stability of the solution, that is in its robustness against small perturbations. Here I present the original, interval-analysis-based family of methods designed for exhaustive exploration of the search space. The power of interval methods makes it possible to reach all mentioned goals within a single, unified framework.
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