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Tutorial Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics)

Posted on 2010-04-14




Name:Tutorial Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics)
ASIN/ISBN:0898714516
Publisher:Society for Industrial Mathematics
Publish Date:Edition: 1987
Pages:390 pages
File size:17.34 Mb
Publisher: Society for Industrial Mathematics
Publish Date: 1987
ISBN: 0898714516
File Type: PDF
Pages: 390 pages
File Size: 17,34 Mb
   Tutorial Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Frontiers in Applied Mathematics)



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

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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