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Automatic Differentiation of Algorithms

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Published by Springer .
Written in English


Book details:

Edition Notes

ContributionsGeorge Corliss (Editor), Christele Faure (Editor), Andreas Griewank (Editor), Laurent Hascoet (Editor), Uwe Naumann (Editor)
The Physical Object
Number of Pages464
ID Numbers
Open LibraryOL7448849M
ISBN 100387953051
ISBN 109780387953052

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Automatic Differentiation of Algorithms provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. The book covers all aspects of the subject: mathematics, scientific programming (i.e., use of adjoints in optimization) and implementation (i.e., memory management problems). Automatic Differentiation of Algorithms provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. BibTeX @BOOK{ GriewankADo, editor = "Andreas Griewank and George F. Corliss", title = "Automatic Differentiation of Algorithms: Theory, Implementation, and Application".   Abstract: Automatic differentiationthe mechanical transformation of numeric computer programs to calculate derivatives efficiently and accuratelydates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine by:

Automatic differentiation of algorithms. By formulating the tensor network algorithm as a computation graph, one can compute higher-order derivatives of the program accurately and efficiently using automatic differentiation. Adjoints and automatic (algorithmic) differentiation in computational finance Cristian Homescu∗ Revised version: May 8, † Two of the most important areas in computational finance: Greeks and, respectively, calibration, are based on efficient and accurate computation of a large number of sensitivities. This paper gives an overviewFile Size: KB. Text books Thomas F. Coleman, Wei Xu: Automatic Differentiation in MATLAB Using ADMAT with Applications SIAM, Uwe Naumann: The Art of Differentiating Computer Programs: An Introduction to Algorithmic Differentiation SIAM, Andreas Griewank, Andrea Walther: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation SIAM,

  The auxiliary function autodiff::wrt, an acronym for with respect to, is used to indicate which input variable (x, y, z) is the selected one to compute the partial derivative of auxiliary function autodiff::at is used to indicate where (at which values of its parameters) the derivative of f is evaluated.. Reverse mode. In a reverse mode automatic differentiation algorithm, the output.   Automatic Differentiation of Algorithms: Theory, Implementation, and Application (Andreas Griewank and George F. Corliss, eds.)Cited by: Automatic Differentiation of Algorithms provides a comprehensive and authoritative survey of all recent developments, new techniques, and tools for AD use. The book covers all aspects of the subject: mathematics, scientific programming (i.e., use of adjoints in optimization) and implementation (i.e., memory management problems).Author: G. Corliss, International Conference on Automatic Di. Introduction. Automatic differentiation (AD) is a set of techniques for transforming a program that calculates numerical values of a function, into a program which calculates numerical values for derivatives of that function with about the same accuracy and efficiency as Cited by: