Showing papers by "Andrea Walther published in 2003"
TL;DR: Two strategies for the implementation of Automatic Differentiation based on the operator overloading facility in C++ are presented and the capabilities of the AD‐tool ADOL‐C that appliesoperator overloading to differentiate C‐ and C++‐code are described.
Abstract: In this paper, we present two strategies for the implementation of Automatic Differentiation (AD) based on the operator overloading facility in C++. Subsequently, we describe the capabilities of the AD-tool ADOL-C that applies operator overloading to differentiate C- and C++-code. Finally, we discuss some applications of ADOL-C.
40 citations
TL;DR: In this article, the authors presented a new area of application for Automatic Differentiation (AD): computing parametric sensitivities for optimization problems, where the term parametric sensitivity refers to the derivative of an optimal solution with respect to the parameters.
Abstract: This article presents a new area of application for Automatic Differentiation (AD): Computing parametric sensitivities for optimization problems. For an optimization problem containing parameters which are not among the optimization variables, the term parametric sensitivity refers to the derivative of an optimal solution with respect to the parameters. We treat non-linear finite- and infinite-dimensional optimization problems, in particular optimal control problems involving ordinary differential equations with control and state constraints, and compute their parametric sensitivities using AD. Particular attention is given to the generation of second-order derivatives required in the process. Copyright © 2003 John Wiley & Sons, Ltd.
22 citations
07 Oct 2003
TL;DR: The authors give a gentle introduction to using various software tools for Automatic Differentiation (AD) and ensure that the content will be kept up-to-date as the AD software covered is evolving.
Abstract: The authors give a gentle introduction to using various software tools for Automatic Differentiation (AD). Ready-to-use examples are discussed and links to further information are presented. The target audience includes all those who are looking for a straight-forward way to get started using the available AD technology. The document is supposed to be dynamic in the sense that its content will be kept up-to-date as the AD software covered is evolving.
1 citations
Posted Content•
TL;DR: In this article, the authors give a gentle introduction to using various software tools for automatic differentiation (AD). Ready-to-use examples are discussed, and links to further information are presented.
Abstract: We give a gentle introduction to using various software tools for automatic differentiation (AD). Ready-to-use examples are discussed, and links to further information are presented. Our target audience includes all those who are looking for a straightforward way to get started using the available AD technology. The document is dynamic in the sense that its content will be updated as the AD software evolves.