Several software systems are available for implementing automatic differentiation of computer programs. The forward mode of automatic differentiation is limited by computational intensity and computer memory. The reverse mode, or adjoint approach, is limited by computer memory and disk storage. A modular technique for derivative computation that can significantly reduce memory required to compute derivatives in a complex FORTRAN model using the reverse mode of automatic differentiation is discussed and demonstrated
Emerging tools for automatic differentiation (AD) of computer programs should be of great benefit fo...
In this paper, we give a simple and efficient implementation of reverse-mode automatic differentiati...
The numerical methods employed in the solution of many scientific computing problems require the com...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic differentiation --- the mechanical transformation of numeric computer programs to calculat...
Automatic dierentiation is a powerful technique for evaluating derivatives of functions given in the...
We propose extensions to FORTRAN which integrate forward and reverse Automatic Differentiation (AD)...
The fast computation of gradients in reverse mode Automatic Differentiation (AD) requires the genera...
Automatic dierentiation is introduced as a powerful technique to compute deriva-tives of functions g...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
summary:Automatic differentiation is an effective method for evaluating derivatives of function, whi...
Automatic differentiation—the mechanical transformation of numeric computer programs to calculate de...
Automatic differentiation is a technique of computing the derivative of a function or a subroutine w...
Emerging tools for automatic differentiation (AD) of computer programs should be of great benefit fo...
In this paper, we give a simple and efficient implementation of reverse-mode automatic differentiati...
The numerical methods employed in the solution of many scientific computing problems require the com...
Full text of this paper is not available in the UHRAThis paper gives an introduction to a number of ...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic differentiation --- the mechanical transformation of numeric computer programs to calculat...
Automatic dierentiation is a powerful technique for evaluating derivatives of functions given in the...
We propose extensions to FORTRAN which integrate forward and reverse Automatic Differentiation (AD)...
The fast computation of gradients in reverse mode Automatic Differentiation (AD) requires the genera...
Automatic dierentiation is introduced as a powerful technique to compute deriva-tives of functions g...
In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evalua...
summary:Automatic differentiation is an effective method for evaluating derivatives of function, whi...
Automatic differentiation—the mechanical transformation of numeric computer programs to calculate de...
Automatic differentiation is a technique of computing the derivative of a function or a subroutine w...
Emerging tools for automatic differentiation (AD) of computer programs should be of great benefit fo...
In this paper, we give a simple and efficient implementation of reverse-mode automatic differentiati...
The numerical methods employed in the solution of many scientific computing problems require the com...