Abstract. The numerical methods employed in the solution of many scientic computing problems require the computation of the gradient of a function f: R n! R. ADIFOR is a source translator that, given a collection of subroutines to compute f, generates Fortran 77 code for computing the derivative of this function. Using the so-called torsion problem from the MINPACK-2 test collection as an example, this paper explores two issues in automatic dierentiation: the ecient computation of derivatives for partial separable functions and the use of the compile-time reverse mode for the generation of derivatives. We show that orders of magnitudes of improvement are possible when exploiting partial separability and maximizing use of the reverse mode.
Developing code for computing the rst- and higher-order derivatives of a function by hand can be ver...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
AbstractWe review the extended Jacobian approach to automatic differentiation of a user-supplied fun...
The numerical methods employed in the solution of many scientific computing problems require the com...
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...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
The accurate and efficient computation of gradients for partially separable functions is central to ...
The numerical methods employed in the solution of many scientific computing problems require the com...
The accurate and ecient computation of gradients for partially separable functions is central to the...
Several software systems are available for implementing automatic differentiation of computer progra...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic dierentiation is a powerful technique for evaluating derivatives of functions given in the...
The context of this work is Automatic Differentiation (AD). Fundamentally, AD transforms a program t...
The advent of robust automatic differentiation tools is an exciting and important development in sci...
Developing code for computing the rst- and higher-order derivatives of a function by hand can be ver...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
AbstractWe review the extended Jacobian approach to automatic differentiation of a user-supplied fun...
The numerical methods employed in the solution of many scientific computing problems require the com...
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...
We study the high order reverse mode of Automatic Differentiation (AD) in the dissertation. Automati...
The accurate and efficient computation of gradients for partially separable functions is central to ...
The numerical methods employed in the solution of many scientific computing problems require the com...
The accurate and ecient computation of gradients for partially separable functions is central to the...
Several software systems are available for implementing automatic differentiation of computer progra...
The numerical methods employed in the solution of many scientific computing problems require the com...
Automatic dierentiation is a powerful technique for evaluating derivatives of functions given in the...
The context of this work is Automatic Differentiation (AD). Fundamentally, AD transforms a program t...
The advent of robust automatic differentiation tools is an exciting and important development in sci...
Developing code for computing the rst- and higher-order derivatives of a function by hand can be ver...
Automatic differentiation provides the foundation for sensitivity analysis and subsequent design opt...
AbstractWe review the extended Jacobian approach to automatic differentiation of a user-supplied fun...