Publication: Automatic Differentiation in MATLAB Using ADMAT with Applications
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Automatic Differentiation in MATLAB Using ADMAT with Applications

- Book -
 

Author(s)
Thomas F. Coleman , Wei Xu

Year
2016

Publisher
SIAM

Abstract
The calculation of partial derivatives is a fundamental need in scientific computing. Automatic differentiation (ad) can be applied straightforwardly to obtain all necessary partial derivatives (usually first and, possibly, second derivatives) regardless of a code’s complexity. However, the space and time efficiency of ad can be dramatically improved---sometimes transforming a problem from intractable to highly feasible---if inherent problem structure is used to apply ad in a judicious manner. Automatic Differentiation in MATLAB Using ADMAT with Applications discusses the efficient use of ad to solve real problems, especially multidimensional zero-finding and optimization, in the MATLAB environment. This book is concerned with the determination of the first and second derivatives in the context of solving scientific computing problems with an emphasis on optimization and solutions to nonlinear systems. The authors focus on the application rather than the implementation of ad, solve real nonlinear problems with high performance by exploiting the problem structure in the application of ad, and provide many easy to understand applications, examples, and MATLAB templates.

AD Theory and Techniques
General, Introduction

BibTeX
@BOOK{
         Coleman2016ADi,
       author = "Thomas F. Coleman and Wei Xu",
       title = "Automatic Differentiation in {MATLAB} Using {ADMAT} with Applications",
       publisher = "SIAM",
       series = "Software, Environments, and Tools",
       address = "Philadelphia, PA",
       isbn = "978-1-611974-35-5",
       ad_theotech = "General, Introduction",
       year = "2016",
       url = "http://bookstore.siam.org/se27/",
       abstract = "The calculation of partial derivatives is a fundamental need in scientific
         computing. Automatic differentiation (AD) can be applied straightforwardly to obtain all necessary
         partial derivatives (usually first and, possibly, second derivatives) regardless of a
         code’s complexity. However, the space and time efficiency of AD can be dramatically
         improved---sometimes transforming a problem from intractable to highly feasible---if inherent
         problem structure is used to apply AD in a judicious manner. \emph{Automatic Differentiation in
         MATLAB Using ADMAT with Applications} discusses the efficient use of AD to solve real problems,
         especially multidimensional zero-finding and optimization, in the MATLAB environment. This book is
         concerned with the determination of the first and second derivatives in the context of solving
         scientific computing problems with an emphasis on optimization and solutions to nonlinear systems.
         The authors focus on the application rather than the implementation of AD, solve real nonlinear
         problems with high performance by exploiting the problem structure in the application of AD, and
         provide many easy to understand applications, examples, and MATLAB templates.",
       number = "27"
}


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