Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation

by Andreas Griewank, Andrea Walther

Algorithmic, or automatic, differentiation (AD) is a growing area of theoretical research and software development concerned with the accurate and efficient evaluation of derivatives for function evaluations given as computer programs. The resulting derivative values are useful for all scientific computations that are based on linear, quadratic, or higher order approximations to nonlinear scalar or vector functions. This second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. There is also added material on checkpointing and iterative differentiation. To improve readability the more detailed analysis of memory and complexity bounds has been relegated to separate, optional chapters. The book consists of: a stand-alone introduction to the fundamentals of AD and its software; a thorough treatment of methods for sparse problems; and final chapters on program-reversal schedules, higher derivatives, nonsmooth problems and iterative processes.

Genres: Technology

460 pages, Paperback

First published January 1, 1987

Reviews

[Simone Scardapane]

Extremely terse and assumes a huge amount of background, but full of insights that are still very relevant today (implicit differentiation, Jacobian simplification, checkpointing, ...).