Synopsis
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In mathematics and computer algebra, automatic differentiation (AD), sometimes alternatively called algorithmic differentiation, is a method to numerically evaluate the derivative of a function specified by a computer program. Two classical methods of doing differentiation are: symbolically differentiate the function as an expression, and evaluate it at the point; or use numerical differentiation. These methods run into problem areas: symbolic differentiation works at low speed, and faces the difficulty of converting a computer program into a single expression, while finite differences can introduce round off errors in the discretization process and cancellation. Both classical methods have problems with calculating higher derivatives, where the complexity and errors increase. Automatic differentiation solves all of these problems. AD exploits the fact that any computer program that implements a vector function y = F(x) (generally) can be decomposed into a sequence of elementary assignments, any one of which may be trivially differentiated by a simple table lookup.
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Présentation de l'éditeur
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In mathematics and computer algebra, automatic differentiation (AD), sometimes alternatively called algorithmic differentiation, is a method to numerically evaluate the derivative of a function specified by a computer program. Two classical methods of doing differentiation are: symbolically differentiate the function as an expression, and evaluate it at the point; or use numerical differentiation. These methods run into problem areas: symbolic differentiation works at low speed, and faces the difficulty of converting a computer program into a single expression, while finite differences can introduce round off errors in the discretization process and cancellation. Both classical methods have problems with calculating higher derivatives, where the complexity and errors increase. Automatic differentiation solves all of these problems. AD exploits the fact that any computer program that implements a vector function y = F(x) (generally) can be decomposed into a sequence of elementary assignments, any one of which may be trivially differentiated by a simple table lookup.
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Automatic Differentiation
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Alphascript Publishing, 2010
ISBN 10: 6130683839
ISBN 13: 9786130683832
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In mathematics and computer algebra, automatic differentiation (AD), sometimes alternatively called algorithmic differentiation, is a method to numerically evaluate the derivative of a function specified by a computer program. Two classical methods of doing differentiation are: symbolically differentiate the function as an expression, and evaluate it at the point; or use numerical differentiation. These methods run into problem areas: symbolic differentiation works at low speed, and faces the difficulty of converting a computer program into a single expression, while finite differences can introduce round off errors in the discretization process and cancellation. Both classical methods have problems with calculating higher derivatives, where the complexity and errors increase. Automatic differentiation solves all of these problems. AD exploits the fact that any computer program that implements a vector function y = F(x) (generally) can be decomposed into a sequence of elementary assignments, any one of which may be trivially differentiated by a simple table lookup. 84 pp. Englisch. Seller Inventory # 9786130683832
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Automatic Differentiation | Mathematics, Computer algebra system, Derivative, Numerical differentiation, Round- off error, Discretization, Chain rule, Gradient, Tangent, Computational complexity theory
Print on DemandSeller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Automatic Differentiation | Mathematics, Computer algebra system, Derivative, Numerical differentiation, Round- off error, Discretization, Chain rule, Gradient, Tangent, Computational complexity theory | Frederic P. Miller (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130683832 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 113237667