  We propose a generalization of separability in the context of global
optimization. Our results apply to objective functions implemented as
differentiable computer programs. They are presented in the context of a simple
branch and bound method. The often significant search space reduction can be
expected to yield an acceleration of any global optimization method. We show
how to utilize interval derivatives calculated by adjoint algorithmic
differentiation to examine the monotonicity of the objective with respect to so
called structural separators and how to verify the latter automatically.
