  We consider convex underestimators that are used in the global optimization
{\alpha}BB method and its variants. The method is based by augmenting the
original nonconvex function by a relaxation term that is derived from an
interval enclosure of the Hessian matrix. In this paper, we discuss the
advantages of symbolic computation of the Hessian matrix. Symbolic computation
often allows simplifications of the resulting expressions, which in turn means
less conservative underestimators. We show by examples that even a small
manipulation with the symbolic expressions, which can be processed
automatically by computers, can have a large effect on the quality of
underestimators.
