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Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics) by Andreas Griewank

Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics)
Title:
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics)
Author:
Andreas Griewank
ISBN:
0898714516
ISBN13:
978-0898714517
Formats available:
mobi txt lit lrf
Category:
Mathematics
Language:
English
Publisher:
Society for Industrial and Applied Mathematics (January 1, 1987)
Pages:
390 pages
PDF size:
1393 kb
FB2 size:
1352 kb
EPUB size:
1798 kb
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.

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