Automatic Differentiation in MATLAB Using ADMAT with Applications


Price:
Sale price$152.00
Stock:
Temporarily out of stock. Order now & we'll deliver when available

By Thomas F. Coleman, Wei Xu
Imprint:
SIAM - SOCIETY FOR INDUSTRIAL AND APPLIED
Release Date:
Format:
PAPERBACK
Dimensions:
229 x 152 mm
Weight:
280 g
Pages:
116

Request Academic Copy

Button Actions

Please copy the ISBN for submitting review copy form

Description

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.

Chapter 1: Fundamentals of Automatic Differentiation and the Use of ADMAT Chapter 2: Products and Sparse Problems Chapter 3: Using ADMAT with the MATLAB Optimization Toolbox Chapter 4: Newton's Method and Optimization Chapter 5: Structure Chapter 6: Combining C/Fortran with ADMAT Chapter 7: AD for Inverse Problems with an Application to Computational Finance Chapter 8: A Template for Structured Problems Chapter 9: R&D Directions Appendix A: Installation of ADMAT Appendix B: How Are Codes Differentiated?

You may also like

Recently viewed