Evaluating Derivatives
Principles and Techniques of Algorithmic Differentiation
Part of Frontiers in Applied Mathematics
- Author: Andreas Griewank, Technische Universität, Dresden
- Date Published: April 2000
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
- format: Paperback
- isbn: 9780898714517
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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.
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×Product details
- Date Published: April 2000
- format: Paperback
- isbn: 9780898714517
- length: 390 pages
- dimensions: 254 x 176 x 18 mm
- weight: 0.684kg
- availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
Table of Contents
Preface
Prologue
Introduction
Part I. Tangents and Gradients. A Framework for Evaluating Functions
Fundamentals of Forward and Reverse
Repeating and Extending Reverse
Implementation and Software
Part II. Jacobians and Hessians. Sparse Forward and Reverse
Exploiting Sparsity by Compression
Going Beyond Forward and Reverse
Observations on Efficiency
Part III. Advances and Reversals. Taylor and Tensor Coefficients
Differentiation without Differentiability
Serial and Parallel Reversal Schedules
Bibliography
Index.
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