Sinossi
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
Risultati della ricerca per Automatic Differentiation
Immagini fornite dal venditore
Automatic Differentiation
Print on DemandDa: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: 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. Codice articolo 9786130683832
Compra nuovo
EUR 34,00
Spedizione EUR 23,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 2 disponibili
Immagini fornite dal venditore
Automatic Differentiation | Mathematics, Computer algebra system, Derivative, Numerical differentiation, Round- off error, Discretization, Chain rule, Gradient, Tangent, Computational complexity theory
Print on DemandDa: preigu, Osnabrück, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: 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. Codice articolo 113237667
Compra nuovo
EUR 109,85
Spedizione EUR 70,00
Spedito da Germania a U.S.A.
Spedito da Germania a U.S.A.
Quantità: 5 disponibili