Название: Introduction to Derivative-Free Optimization
Серия: MPS-SIAM series on optimization, 8
Автор: Andrew R. Conn, Katya Scheinberg, Luís N. Vicente
Издательство: Society for Industrial and Applied Mathematics
Год: 2009
Формат: pdf
Страниц: 290
Размер: 11,1 Мб
Язык: английский
The absence of derivatives, often combined with the presence of noise or lack of smoothness, is a major challenge for optimization. This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems. Although readily accessible to readers with a modest background in computational mathematics, it is also intended to be of interest to researchers in the field. Introduction to Derivative-Free Optimization is the first contemporary comprehensive treatment of optimization without derivatives. This book covers most of the relevant classes of algorithms from direct search to model-based approaches. It contains a comprehensive description of the sampling and modeling tools needed for derivative-free optimization; these tools allow the reader to better analyze the convergent properties of the algorithms and identify their differences and similarities.
