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Book synopsis

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.

Book details

Book category Books in English Mathematics & science Mathematics Optimization

• Full title: Introduction to Derivative-Free Optimization
• Author: Andrew R. ConnKatya ScheinbergLuís N. Vicente
• Language: English
• Binding: Paperback
• Number of pages: 289
• EAN: 9780898716689
• ISBN: 0898716683
• ID: 02050226
• Publisher: Society for Industrial & Applied Mathematics,U.S.
• Weight: 53 g
• Dimensions: 256 × 182 × 18 mm
• Date of publishing: 30. January 2008

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