Can automatic differentiation be used on the parameters of an optimization problem?

If I wanted to perform an optimization using a Newton-based solver where the Hessian and gradient of a function are known analytically, and then use a package such as Adept to compute a Jacobian matrix ("reverse mode") with respect to the optimization parameters, would that be possible? Would there be excessive memory usage if the optimization takes several thousand steps to converge?