Abstract
Any decision problem (with an objective function f to be minimized or maximized) may be classified as a global optimization problem, if there is no additional information indicating that there is only one minimum (or maximum). This definition also includes the case of discrete optimization, when the (quantifiable) decision variables must assume discrete values. In this book we shall consider only the case of continuous variables because for the purpose of developing solutions the discrete optimization problems are better regarded separately.
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© 1989 Kluwer Academic Publishers
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Mockus, J. (1989). Global Optimization and the Bayesian Approach. In: Bayesian Approach to Global Optimization. Mathematics and Its Applications, vol 37. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0909-0_1
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DOI: https://doi.org/10.1007/978-94-009-0909-0_1
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6898-7
Online ISBN: 978-94-009-0909-0
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