Global optimization subject to bound constraints helps answer many practical questions in chemistry, molecular biology, economics. Most of algorithms for solution of global optimization problems are a combination of interval methods and exhaustive search. The efficiency of such algorithms is characterized by their ability to detect and eliminate sub-optimal feasible regions. This ability is increased by availability of a good upper bound on the global minimurn. In this paper, we present a symbolic-interval algorithm for calculation of upper bounds in bound-constrained global minimization problems and report the results of some experiments.
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