This paper describes approaches to computing second-order derivatives with automatic differentiation (AD) based on the forward mode and the propagation of univariate Taylor series. Performance results are given which show the speedup possible with these techniques. We also describe a new source transformation AD module for computing secondorder derivatives of C and Fortran codes and the underlying infrastructure used to create a language-independent translation tool
ADIFOR provides a simple means to produce code for the first derivatives of functions through the te...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
This article provides a short overview of the theory of First Order Automatic Differentiation (AD) f...
This article describes approaches to computing second-order derivatives with automatic differentiati...
Many algorithms for scientific computation require second- or higher-order partial derivatives, whic...
Many algorithms for scientic computation require second- or higher-order partial derivatives, which ...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
The numerical methods employed in the solution of many scientific computing problems require the com...
In this paper, we introduce automatic differentiation as a method for computing derivatives of large...
Automatic differentiation is a technique of computing the derivative of a function or a subroutine w...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
In this paper, we give a simple and efficient implementation of reverse-mode automatic differentiati...
ADIFOR provides a simple means to produce code for the first derivatives of functions through the te...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
This article provides a short overview of the theory of First Order Automatic Differentiation (AD) f...
This article describes approaches to computing second-order derivatives with automatic differentiati...
Many algorithms for scientific computation require second- or higher-order partial derivatives, whic...
Many algorithms for scientic computation require second- or higher-order partial derivatives, which ...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
The numerical methods employed in the solution of many scientific computing problems require the com...
In this paper, we introduce automatic differentiation as a method for computing derivatives of large...
Automatic differentiation is a technique of computing the derivative of a function or a subroutine w...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
In this paper, we give a simple and efficient implementation of reverse-mode automatic differentiati...
ADIFOR provides a simple means to produce code for the first derivatives of functions through the te...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
This article provides a short overview of the theory of First Order Automatic Differentiation (AD) f...