An Introduction to Zero-Suppressed Binary Decision Diagrams

Test Pattern Generation for Multiple Stuck-at Faults Using Zero Suppressed Binary Decision Diagrams

Chain Reduction for Binary and Zero-Suppressed Decision Diagrams

Chain reduction enables reduced ordered binary decision diagrams (BDDs) and zero-suppressed binary decision diagrams (ZDDs) to each take advantage of the others’ ability to symbolically represent Boolean functions in compact form. For any Boolean function, its chain-reduced ZDD (CZDD) representation will be no larger than its ZDD representation, and at most twice the size of its BDD representat...

Zero-Suppressed Computation: A New Computation Inspired by ZDDs

Zero-suppressed binary decision diagrams (ZDDs) are a data structure representing Boolean functions, and one of the most successful variants of binary decision diagrams (BDDs). On the other hand, BDDs are also called branching programs in computational complexity theory, and have been studied as a computation model. In this paper, we consider ZDDs from the viewpoint of computational complexity ...

Mining simple and complex patterns efficiently using Binary Decision Diagrams

Pattern mining is a knowledge discovery task which is useful for finding interesting data characteristics. Existing mining techniques sometimes suffer from limited performance in challenging situations, such as when finding patterns in high-dimensional datasets. Binary Decision Diagrams and their variants are a compact and efficient graph data structure for representing and manipulating boolean...

The Theory of Zero - Suppressed

| Zero{suppressed binary decision diagrams (ZBDDs) have been introduced by Minato in 14]{{17]. Here the structural properties of ZBDDs are worked out and a generic synthesis algorithm is presented and analyzed. It is proved that ZBDDs can be at most by a factor n+1 smaller or larger than ordered BDDs (OBDDs) for the same function on n variables. Using ZBDDs the best known bounds on the number o...