NitroFlare Introduction to Derivative-Free Optimization

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    kocogi Active Member

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    Andrew R. Conn, Katya Scheinberg, Luís N. Vicente, "Introduction to Derivative-Free Optimization"
    English | 2009 | ISBN: 0898716683 | PDF | pages: 289 | 1,9 mb


    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. 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. 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.[​IMG]
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