In a series of articles Gupta et al. develop a framework for automatic test data generation for computer programs. In general, their approach consists of a branch predicate collector, which derives a system of linear inequalities representing the branch predicates for a given path in the program. This system is solved using a solving technique of theirs called the Unified Numerical Approach (UNA) [5, 7]. In this paper we show that in contrast to traditional optimization methods the UNA is not bounded by the size of the solved system. Instead it depends on how input is composed. That is, even for very simple systems consisting of one variable we can easily get more than a thousand iterations. We will also give a formal proof that UNA does not always find a mixed integer solution when there is one. Finally, we suggest using some traditional optimization method instead, like the simplex method in combination with branch-and-bound and/or a cutting-plane algorithm as a constraint solver.