Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation: 19 (Frontiers in Applied Mathematics, Series Number 19) - Softcover

Griewank, Andreas

Softcover

ISBN 10: 0898714516 ISBN 13: 9780898714517

Publisher: Society for Industrial and Applied Mathematics, 1987

Synopsis

Algorithmic, or automatic, differentiation (AD) is concerned with the accurate and efficient evaluation of derivatives for functions defined by computer programs. No truncation errors are incurred, and the resulting numerical derivative values can be used for all scientific computations that are based on linear, quadratic, or even higher order approximations to nonlinear scalar or vector functions. In particular, AD has been applied to optimization, parameter identification, equation solving, the numerical integration of differential equations, and combinations thereof. Apart from quantifying sensitivities numerically, AD techniques can also provide structural information, e.g., sparsity pattern and generic rank of Jacobian matrices.

About this title

Book Description

The book is divided into three parts: a stand-alone introduction to the fundamentals of AD and its software, a thorough treatment of methods for sparse problems, and final chapters on higher derivatives, nonsmooth problems, and program reversal schedules.

About the Author

Andreas Griewank is a former senior scientist of Argonne National Laboratory and authored the first edition of this book in 2000. He holds a Ph.D. from the Australian National University and is currently Deputy Director of the Institute of Mathematics at Humboldt University Berlin and a member of the DFG Research Center Matheon, Mathematics for Key Technologies. His main research interests are nonlinear optimization and scientific computing.

Edition details

• Publisher: Society for Industrial and Applied Mathematics
• Publication date: 1987
• Language: English
• ISBN 10: 0898714516
• ISBN 13: 9780898714517
• Binding: Paperback
• Number of pages: 390