Automatic Differentiation in MATLAB using ADMAT with Applications
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-435-5 (ISBN)
The calculation of partial derivatives is fundamental in scientific computing. Automatic differentiation (AD) can be applied straightforwardly to obtain all necessary partial 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. Discussing the efficient use of AD to solve real problems in the MATLAB environment, especially multidimensional zero-finding and optimization, this book is concerned with determination of the first and second derivatives, with emphasis placed on optimization and solutions to nonlinear systems. The authors focus on the application, rather than the implementation, of AD and solve real nonlinear problems with high performance by exploiting the problem structure in AD's application. Many easy-to-understand applications, examples, and MATLAB templates are provided, meaning this book will prove useful to financial engineers, quantitative analysts, and researchers.
Thomas F. Coleman is a Professor in the Department of Combinatorics and Optimization, as well as the Ophelia Lazaridis University Research Chair, at the University of Waterloo. He is also the Director of WatRISQ, an institute composed of finance researchers that spans several faculties at the university. From 2005 to 2010, Dr Coleman was Dean of the Faculty of Mathematics at the University of Waterloo. Prior to this, he was Professor of Computer Science at Cornell University. He was also Director of the Cornell Theory Center (CTC), a supercomputer applications center, and founded and directed CTC-Manhattan, a computational finance venture. Dr Coleman has authored three books on computational mathematics, edited six conference proceedings, and published over 80 journal articles in the areas of optimization, automatic differentiation, parallel computing, computational finance, and optimization applications. Wei Xu is Research Manager at the Global Risk Institute (GRI), Toronto. Before joining GRI, Dr Xu was a Visiting Professor at the University of Waterloo. Previously, he was an Associate Professor at Tongji University, Shanghai. He co-founded Shanghai Raiyun Information Technology Ltd, a risk management services and solutions provider, and currently serves as its Director of R&D. His research has been featured in over 30 publications and he has co-authored a book on risk management.
Preface; 1. Fundamentals of automatic differentiation and the use of ADMAT; 2. Products and sparse problems; 3. Using ADMAT with the MATLAB optimization toolbox; 4. Newton's method and optimization; 5. Structure; 6. Combining C/Fortran with ADMAT; 7. AD for inverse problems with an application to computational finance; 8. A template for structured problems; 9. R&D directions; Appendix A. Installation of ADMAT; Appendix B. How are codes differentiated?; Bibliography; Index.
| Erscheinungsdatum | 01.09.2016 |
|---|---|
| Reihe/Serie | Software, Environments and Tools |
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 178 x 253 mm |
| Gewicht | 270 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Analysis |
| ISBN-10 | 1-61197-435-6 / 1611974356 |
| ISBN-13 | 978-1-61197-435-5 / 9781611974355 |
| Zustand | Neuware |
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