Dynamic Minimization of Word - Level Decision

Word-Level Decision Diagrams (WLDDs), like *BMDs and K*BMDs, have recently been introduced as a data structure for veriication. The size of WLDDs largely depends on the chosen variable ordering , i.e. the ordering in which variables are encountered , and on the decompositions carried out in each node. In this paper we present a framework for dynamic minimization of WLDDs. We discuss the dii-culties with previous techniques if applied to WLDDs and present a new approach that eeciently adapts both variable ordering and decomposition type choice. Experimental results demonstrate that this method out-performs \classical" reordering with respect to run-time and representation size during dynamic minimization of word-level functions.