Modern methods for numerical optimization calculate (or approximate) the matrix of second derivatives, the Hessian matrix, at each iteration. The recent arrival of robust software for automatic differentiation allows for the possibility of automatically computing the Hessian matrix, and the gradient, given a code to evaluate the objective function itself. However, for large-scale problems direct application of automatic differentiation may be unacceptably expensive. Recent work has shown that this cost can be dramatically reduced in the presence of sparsity. In this paper we show that for structured problems it is possible to apply automatic differentiation tools in an economical way - even in the absence of sparsity in the Hessian.