Minimization of memory size for heterogeneous MDDs

Abstract

In this paper, we pmpose exact and heuristic algorithms for minimizing the memory size for heterogeneous Multivalued Decision Diagrams (MDh). In a heterogeneous MDD, each multi-valued variable can take a different domain. To represen1 a binary logic hnclion using a heterogeneous MDD, we partition the binary variables into gmnps, and treat the groups as multi-valued variables. Therefore, the memory size of a hetemgeneous MDD depends on the partition of the binary variables. Our experimental results show that heterogeneous MDDs repuim smaller memory size than Reduced Ordered Binary Decision Diagrams (ROBDDs) and Free BDDs (FBDDs).