Introduction to Derivative-Free Optimization

This book is the first contemporary comprehensive treatment of optimization without derivatives, and it covers most of the relevant classes of algorithms from direct-search to model-based approaches. Readily accessible to readers with a modest background in computational mathematics, Introduction to Derivative-Free Optimization contains:

• a comprehensive description of the sampling and modeling tools needed for derivative-free optimization that allow the reader to better understand the convergent properties of the algorithms and identify their differences and similarities;

• analysis of convergence for modified Nelder–Mead and implicit-filtering methods as well as for model-based methods such as wedge methods and methods based on minimum–norm Frobenius models.