  We discuss the role of automatic differentiation tools in optimization
software. We emphasize issues that are important to large-scale optimization
and that have proved useful in the installation of nonlinear solvers in the
NEOS Server. Our discussion centers on the computation of the gradient and
Hessian matrix for partially separable functions and shows that the gradient
and Hessian matrix can be computed with guaranteed bounds in time and memory
requirements
