The unknowns of a given problem are typically represented in its model by constrained variables. There are practical ways of cutting down on the number of variables and thus reducing the size of the model and its search space.

Problems best solved with constraint-based programming are generally subject to intrinsic combinatorial growth. Even if reducing the domains of variables by propagating constraints makes it possible to reduce the search space, the initial size of this search space still remains a weighty factor in the execution time.