Evaluating Derivatives Principles and Techniques of Algorithmic Differentiation, Second Edition
Evaluating Derivatives Principles and Techniques of Algorithmic Differentiation, Second Edition by Andreas Griewank is published by SIAM and was released on November 6, 2008. This edition spans 459 pages and is presented in English. The book focuses on algorithmic differentiation (AD), a field dedicated to the precise and efficient evaluation of derivatives derived from computer programs, which is essential for various scientific computations involving nonlinear functions.
Readers will find a comprehensive exploration of both the theoretical and practical aspects of AD, including recent advancements in applications and theory. The text includes a stand-alone introduction to the fundamentals of AD, methods for sparse problems, and discussions on program-reversal schedules and higher derivatives. Additional topics such as checkpointing and iterative differentiation are also covered, with detailed analyses of memory and complexity bounds provided in optional chapters. This edition serves as a valuable resource for those interested in mathematics, programming, and optimization techniques.
Official synopsis Publisher
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.
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