Review of the book ``Derivative-Free and Blackbox Optimization'' by C. Audet and W. Hare, Springer Series in Operations Research and Financial Engineering, 2017, 302 pages, DOI 10.1007/978-3-319-68913-5

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In the interest of full disclosure, the reader is advised that I am biased positively towards the book considered here as I have collaborated with its first author several times during the last 19 years. In fact, the two of us served as guest editors of the last special issue on derivative-free and blackbox optimization appeared in this journal Audet and Kokkolaras (2016).

As an engineer, my primary appreciation of this book pertains to its invaluable contribution to the field of engineering design optimization. Numerical optimization problems in the modern era of computer-aided engineering design rely almost exclusively on simulation models to evaluate objective and constraint functions. Therefore, gradients may either not exist or require unreasonable and unjustifiable effort to be approximated with adequate precision if automatic differentiation is not an option. This is frequently the case with so called blackboxes, i.e., computational (we use the terms "computational" and "simulation" interchangeably to characterize a model) models that the user does not have access to. The only resort then is to use derivative-free optimization algorithms.

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