The Complexity of Integer Bound Propagation
This resolves a fundamental complexity question for AI researchers and practitioners in Constraint Programming, showing that efficient exact algorithms are unlikely, which is a foundational result.
The paper tackled the problem of whether strongly-polynomial algorithms exist for computing the common bound consistent fixpoint in bound propagation, a key technique in Constraint Programming, and proved that this computation is NP-complete, even for binary linear constraints.
Bound propagation is an important Artificial Intelligence technique used in Constraint Programming tools to deal with numerical constraints. It is typically embedded within a search procedure ("branch and prune") and used at every node of the search tree to narrow down the search space, so it is critical that it be fast. The procedure invokes constraint propagators until a common fixpoint is reached, but the known algorithms for this have a pseudo-polynomial worst-case time complexity: they are fast indeed when the variables have a small numerical range, but they have the well-known problem of being prohibitively slow when these ranges are large. An important question is therefore whether strongly-polynomial algorithms exist that compute the common bound consistent fixpoint of a set of constraints. This paper answers this question. In particular we show that this fixpoint computation is in fact NP-complete, even when restricted to binary linear constraints.