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Introduction to derivative-free optimization

Part of the MOS-SIAM series on optimization series
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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 understand the convergent properties of the algorithms and identify their differences and similarities. Introduction to Derivative-Free Optimization also contains 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.

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Product Details
0898716683 / 9780898716689
Paperback
519.6
30/01/2008
United States
English
295 p.
26 cm
Professional & Vocational Learn More