  Many functions characterising physical systems are additively separable. This
is the case, for instance, of mechanical Hamiltonian functions in physics,
population growth equations in biology, and consumer preference and utility
functions in economics. We consider the scenario in which a surrogate of a
function is to be tested for additive separability. The detection that the
surrogate is additively separable can be leveraged to improve further learning.
Hence, it is beneficial to have the ability to test for such separability in
surrogates. The mathematical approach is to test if the mixed partial
derivative of the surrogate is zero; or empirically, lower than a threshold. We
present and comparatively and empirically evaluate the eight methods to compute
the mixed partial derivative of a surrogate function.
