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Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation

Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation

Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
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Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation Paperback - 2008

by Griewank, Andreas

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Details

  • Title Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
  • Author Griewank, Andreas
  • Binding Paperback
  • Edition 2nd ed.
  • Condition Used - Good
  • Pages 460
  • Volumes 1
  • Language ENG
  • Publisher Society for Industrial and Applied Mathematic
  • Publication date 2008-09
  • Illustrated Yes
  • Bookseller's Inventory # 0898716594.G
  • ISBN 9780898716597 / 0898716594
  • Weight 1.79 lbs (0.81 kg)
  • Dimensions 9.72 x 6.85 x 0.79 in (24.69 x 17.40 x 2.01 cm)
  • Category Mathematics
  • Library of Congress subjects Differential calculus - Data processing, Einf'uhrung
  • Library of Congress Catalogue Number 2008021064
  • Dewey Decimal Code 515.33
  • Quantity available 1

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Reader reviews for Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation

From the publisher

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. This second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. 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: 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.
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