Automatic Differentiation

Automatic Differentiation (AD) is a maturing computational technology and has become a mainstream tool used by practicing scientists and computer engineers. The rapid advance of hardware computing power and AD tools has enabled practitioners to quickly generate derivative-enhanced versions of their code for a broad range of applications in applied research and development.

The Fourth International Conference on Automatic Differentiation was held July 20-23 in Chicago, Illinois. The conference included a one day short course, 42 presentations, and a workshop for tool developers. This gathering of auto- matic differentiation researchers extended a sequence that began in Brecken- ridge, Colorado, in 1991 and continued in Santa Fe, New Mexico, in 1996 and Nice, France, in 2000. We invited conference participants and the general au- tomatic differentiation community to submit papers to this special collection. The 28 accepted papers reflect the state of the art in automatic differentiation.

Automatic Differentiation (AD) is a maturing computational technology and has become a mainstream tool used by practicing scientists and computer engineers. The rapid advance of hardware computing power and AD tools has enabled practitioners to quickly generate derivative-enhanced versions of their code for a broad range of applications in applied research and development.