A method is presented for computing all higher-order partial derivatives of a multivariate function Rn → R. This method works by evaluating the function under a nonstandard interpretation, lifting reals to multivariate power series. Multivariate power series, with potentially an infinite number of terms with nonzero coefficients, are represented using a lazy data structure constructed out of linear terms. A complete implementation of this method in SCHEME is presented, along with a straightforward exposition, based on Taylor expansions, of the method’s correctness
AbstractA new type of Taylor series based finite difference approximations of higher-degree derivati...
International audienceInterpretation methods and their restrictions to polynomials have been deeply ...
Copyright © Australian Mathematical Society This paper is made available with the permission of the ...
A method is presented for computing all higher-order partial derivatives of a multivariate function...
A method is presented for computing all higher-order partial derivatives of a multivariate function ...
AbstractA formal algorithm is given for the systematic exact evaluation of higher order partial deri...
We discuss the augmentation of a functional-programming language with a derivative-taking operator i...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
We present semantic correctness proofs of automatic differentiation (AD). We consider a forward-mode...
Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tang...
AbstractIn a recent paper an algorithm FEED was introduced for the systematic exact evaluation of hi...
We present semantic correctness proofs of automatic differentiation (AD). We consider a forward-mode...
We discuss the augmentation of a functional-programming language with a derivative- taking operator ...
This article provides a short overview of the theory of First Order Automatic Differentiation (AD) f...
AbstractA new type of Taylor series based finite difference approximations of higher-degree derivati...
International audienceInterpretation methods and their restrictions to polynomials have been deeply ...
Copyright © Australian Mathematical Society This paper is made available with the permission of the ...
A method is presented for computing all higher-order partial derivatives of a multivariate function...
A method is presented for computing all higher-order partial derivatives of a multivariate function ...
AbstractA formal algorithm is given for the systematic exact evaluation of higher order partial deri...
We discuss the augmentation of a functional-programming language with a derivative-taking operator i...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
We present semantic correctness proofs of automatic differentiation (AD). We consider a forward-mode...
Where dual-numbers forward-mode automatic differentiation (AD) pairs each scalar value with its tang...
AbstractIn a recent paper an algorithm FEED was introduced for the systematic exact evaluation of hi...
We present semantic correctness proofs of automatic differentiation (AD). We consider a forward-mode...
We discuss the augmentation of a functional-programming language with a derivative- taking operator ...
This article provides a short overview of the theory of First Order Automatic Differentiation (AD) f...
AbstractA new type of Taylor series based finite difference approximations of higher-degree derivati...
International audienceInterpretation methods and their restrictions to polynomials have been deeply ...
Copyright © Australian Mathematical Society This paper is made available with the permission of the ...