In engineering applications, we often need the derivatives of functions defined by a program. The approach chosen for derivative computation must be algebraic to allow computer implementation. A particular solution to obtain first derivatives is the application of dual numbers. This paper proposes simple and compact generalizations of this idea to obtain derivatives of arbitrary order for single or multi-variate functions and the automatic handling of 0/0 ambiguities in the calculations. We also provide the C++ code that takes advantage of operator overloading and recursion. The method is demonstrated by path animation, Gaussian curvature computation, and curve fairing
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
The Mad package described here facilitates the evaluation of first derivatives of multi-dimensional...
AbstractIn this paper, we introduce an algorithm and a computer code for numerical differentiation o...
In engineering applications, we often need the derivatives of functions defined by a program. The ap...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
The calculation of derivatives is ubiquitous in science and engineering. In thermodynamics, in parti...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
AbstractIn a recent paper an algorithm FEED was introduced for the systematic exact evaluation of hi...
It is commonly assumed that calculating third order information is too expensive for most applicatio...
In this article we present a new approach for automatic adjoint differentiation (AAD) with a specia...
We present semantic correctness proofs of automatic differentiation (AD). We consider a forward-mode...
We present an example of the science that is enabled by object-oriented programming techniques. Scie...
The C++ package ADOL-C described in this paper facilitates the evaluation of first and higher deriva...
Developing code for computing the first- and higher-order derivatives of a function by hand can be v...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
The Mad package described here facilitates the evaluation of first derivatives of multi-dimensional...
AbstractIn this paper, we introduce an algorithm and a computer code for numerical differentiation o...
In engineering applications, we often need the derivatives of functions defined by a program. The ap...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
The calculation of derivatives is ubiquitous in science and engineering. In thermodynamics, in parti...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
AbstractIn a recent paper an algorithm FEED was introduced for the systematic exact evaluation of hi...
It is commonly assumed that calculating third order information is too expensive for most applicatio...
In this article we present a new approach for automatic adjoint differentiation (AAD) with a specia...
We present semantic correctness proofs of automatic differentiation (AD). We consider a forward-mode...
We present an example of the science that is enabled by object-oriented programming techniques. Scie...
The C++ package ADOL-C described in this paper facilitates the evaluation of first and higher deriva...
Developing code for computing the first- and higher-order derivatives of a function by hand can be v...
This article provides an overview of some of the mathematical prin- ciples of Automatic Differentia...
The Mad package described here facilitates the evaluation of first derivatives of multi-dimensional...
AbstractIn this paper, we introduce an algorithm and a computer code for numerical differentiation o...