Composition trees in finding best variable orderings for ROBDDs

Summary form only given. The algorithms for static reordering of Reduced Ordered Binary Decision Diagrams (ROBDDs) rely on dependable properties for grouping of variables. Two such properties have been studied so far: keeping symmetric variables adjacent and minimizing the ROBDD width. However, counterexamples have been found for the both cases. In this paper, we introduce a new condition for grouping of variables, suggesting to keep adjacent the variables from all bound sets of the function which are explicitly given by its composition tree.