Many algorithms for scientific computation require second- or higher-order partial derivatives, which can be efficiently computed by a propagating a set of univariate Taylor series. We describe how to implement second-order mixed partial derivative computations in ADIFOR (Automatic Differentiation In FORtran), a Fortran-to-Fortran source transformation tool. Globally, we propagate three-term univariate Taylor series in the forward mode. Locally, we preaccumulate local gradients and Hessians for complicated expressions on the right-hand sides of assignment statements. We describe the source transformations and give an example of the transformed code
80 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1980.If the gradient of the functio...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
In thisarticle we consider the problem of computing approximations to the second derivatives of func...
Many algorithms for scientic computation require second- or higher-order partial derivatives, which ...
This article describes approaches to computing second-order derivatives with automatic differentiati...
This paper describes approaches to computing second-order derivatives with automatic differentiation...
1 1 Introduction 1 2 Procedure 1 3 Discussion 2 4 Initialization 3 5 Example 4 6 Conclusions 8 Appen...
ADIFOR provides a simple means to produce code for the first derivatives of functions through the te...
The numerical methods employed in the solution of many scientific computing problems require the com...
Second- and higher-order derivatives are required by applications in scientic computation, espe-cial...
The numerical methods employed in the solution of many scientific computing problems require the com...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
AbstractIn a recent paper an algorithm FEED was introduced for the systematic exact evaluation of hi...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
80 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1980.If the gradient of the functio...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
In thisarticle we consider the problem of computing approximations to the second derivatives of func...
Many algorithms for scientic computation require second- or higher-order partial derivatives, which ...
This article describes approaches to computing second-order derivatives with automatic differentiati...
This paper describes approaches to computing second-order derivatives with automatic differentiation...
1 1 Introduction 1 2 Procedure 1 3 Discussion 2 4 Initialization 3 5 Example 4 6 Conclusions 8 Appen...
ADIFOR provides a simple means to produce code for the first derivatives of functions through the te...
The numerical methods employed in the solution of many scientific computing problems require the com...
Second- and higher-order derivatives are required by applications in scientic computation, espe-cial...
The numerical methods employed in the solution of many scientific computing problems require the com...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
AbstractIn a recent paper an algorithm FEED was introduced for the systematic exact evaluation of hi...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
80 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1980.If the gradient of the functio...
Automatic differentiation of third order derivatives is implemented in C++. The implementation uses ...
In thisarticle we consider the problem of computing approximations to the second derivatives of func...