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. AD has been applied in particular to optimization, parameter identification, nonlinear equation solving, the ...
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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. AD has been applied in particular to optimization, parameter identification, nonlinear equation solving, the numerical integration of differential equations, and combinations of these. Apart from quantifying sensitivities numerically, AD also yields structural dependence information, such as the sparsity pattern and generic rank of Jacobian matrices. The field opens up an exciting opportunity to develop new algorithms that reflect the true cost of accurate derivatives and to use them for improvements in speed and reliability. This second edition has been updated and expanded to cover recent developments in applications and theory, including an elegant NP completeness argument by Uwe Naumann and a brief introduction to scarcity, a generalization of sparsity. 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 three parts: 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. Each of the 15 chapters concludes with examples and exercises.
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Add this copy of Evaluating Derivatives: Principles and Techniques of to cart. $110.62, good condition, Sold by BuenaWave rated 5.0 out of 5 stars, ships from Oklahoma City, OK, UNITED STATES, published 2008 by Society for Industrial and Applied Mathematics.
Edition:
2008, Society for Industrial and Applied Mathematics (SIAM)
Publisher:
Society for Industrial and Applied Mathematics (SIAM)
Published:
2008
Language:
English
Alibris ID:
18489118865
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Add this copy of Evaluating Derivatives to cart. $119.74, new condition, Sold by Media Smart rated 4.0 out of 5 stars, ships from Hawthorne, CA, UNITED STATES, published 2008 by Society for Industrial & Applied Mathematics.
Edition:
2008, Society for Industrial and Applied Mathematics (SIAM)
Add this copy of Evaluating Derivatives: Principles and Techniques of to cart. $190.69, good condition, Sold by Bonita rated 4.0 out of 5 stars, ships from Santa Clarita, CA, UNITED STATES, published 2008 by Society for Industrial and App.
Edition:
2008, Society for Industrial and Applied Mathematics (SIAM)
