By Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke

ISBN-10: 3540689354

ISBN-13: 9783540689355

ISBN-10: 3540689427

ISBN-13: 9783540689423

This assortment covers advances in automated differentiation conception and perform. desktop scientists and mathematicians will find out about fresh advancements in automated differentiation thought in addition to mechanisms for the development of strong and strong automated differentiation instruments. Computational scientists and engineers will enjoy the dialogue of varied functions, which offer perception into potent recommendations for utilizing computerized differentiation for inverse difficulties and layout optimization.

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Extra info for Advances in Automatic Differentiation (Lecture Notes in Computational Science and Engineering)

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Giles 14. : Linear statistical inference and its applications. Wiley, New York (1973) 15. : Matrix derivatives. Marcel Dekker, New York (1980) 16. : Using complex variables to estimate derivatives of real functions. SIAM Review 10(1), 110–112 (1998) 17. : An introduction to multivariate statistics. North Holland, New York (1979) 18. : ADMAT: automatic differentiation in MATLAB using object oriented methods. In: SIAM Interdiscplinary Workshop on Object Oriented Methods for Interoperability, pp. 174–183.

Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation. No. 19 in Frontiers in Appl. Math. SIAM, Philadelphia, PA (2000) 9. 1 user’s guide. INRIA Sophia Antipolis, 2004, Route des Lucioles, 09902 Sophia Antipolis, France (2004). See http://www. html 10. : Logic in Computer Science: Modelling and Reasoning about Systems. Cambridge University Press, Cambridge, England (2000) 11. : Formal certification of a compiler back-end or: programming a compiler with a proof assistant.

In order to establish the link between strict data-flow reversal and adjoint codes one needs to construct numerical programs whose adjoints exhibit a suitable data access pattern. This is done in [13]. Compiler-based code generation needs to be conservative. It is based on some sort of call graph possibly resulting in different call trees for varying values of the program’s inputs. Such call trees do not exists at compile time. The solutions to a generally undecidable problem yield a computationally hard problem.

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Advances in Automatic Differentiation (Lecture Notes in Computational Science and Engineering) by Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke

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