Publisher description for Evaluating derivatives : principles and techniques of algorithmic differentiation / Andreas Griewank.

Bibliographic record and links to related information available from the Library of Congress catalog

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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.

Library of Congress subject headings for this publication:
Differential calculus -- Data processing.