  In developing theories of nuclear binding energy such as density-functional
theory, the effort required to make a fit can be daunting due to the large
number of parameters that may be in the theory and the large number of nuclei
in the mass table. For theories based on the Skyrme interaction, the effort can
be reduced considerably by using the singular value decomposition to reduce the
size of the parameter space. We find that the sensitive parameters define a
space of dimension four or so, and within this space a linear refit is adequate
for a number of Skyrme parameters sets from the literature. We do not find
marked differences in the quality of the fit between the SLy4, the Bky4 and SkP
parameter sets. The r.m.s. residual error in even-even nuclei is about 1.5 MeV,
half the value of the liquid drop model. We also discuss an alternative norm
for evaluating mass fits, the Chebyshev norm. It focuses attention on the cases
with the largest discrepancies between theory and experiment. We show how it
works with the liquid drop model and make some applications to models based on
Skyrme energy functionals. The Chebyshev norm seems to be more sensitive to new
experimental data than the root-mean-square norm. The method also has the
advantage that candidate improvements to the theories can be assessed with
computations on smaller sets of nuclei.
