New book, Automatic Differentiation in MATLAB Using ADMAT with Applications‏

Announcing the June 20, 2016, publication by SIAM of:

Automatic Differentiation in MATLAB Using ADMAT with Applications by Thomas F. 
Coleman and Wei Xu


xii + 105 pages / Softcover / ISBN 978-1-611974-35-5 / List Price $59.00 / 
SIAM Member Price $41.30 / Order Code SE27


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.


This book will prove useful to financial engineers, quantitative analysts, and researchers 
working with inverse problems, as well as to engineers and applied scientists in other fields.


To order or for more about this book, including links to its Table of Contents, Preface, and 
Index, please visit