  We consider optimization problems that arise when estimating a set of unknown
parameters from experimental data, particularly in the context of nuclear
density functional theory. We examine the cost of not having derivatives of
these functionals with respect to the parameters. We show that the POUNDERS
code for local derivative-free optimization obtains consistent solutions on a
variety of computationally expensive energy density functional calibration
problems. We also provide a primer on the operation of the POUNDERS software in
the Toolkit for Advanced Optimization.
