The Nonapproximability of OBDD Minimization

Learning Heuristics for Obdd Minimization by Evolutionary Algorithms Learning Heuristics for Obdd Minimization by Evolutionary Algorithms

Ordered Binary Decision Diagrams (OBDDs) are the state-of-the-art data structure in CAD for ICs. OBDDs are very sensitive to the chosen variable ordering, i.e. the size may vary from linear to exponential. In this paper we present an Evolutionary Algorithm (EA) that learns good heuristics for OBDD minimization starting from a given set of basic operations. The diierence to other previous approa...

Learning Heuristics for OBDD Minimization by Evolutionary Algorithms

Ordered Binary Decision Diagrams (OBDDs) are the state-of-the-art data structure in CAD for ICs. OBDDs are very sensitive to the chosen variable ordering, i.e. the size may vary from linear to exponential. In this paper we present an Evolutionary Algorithm (EA) that learns good heuristics for OBDD minimization starting from a given set of basic operations. The diierence to other previous approa...

Heuristics for ⊕-OBDD Minimization

On the Existence of Polynomial Time Approximation Schemes for OBDD Minimization (Extended Abstract)

The size of Ordered Binary Decision Diagrams (OBDDs) is determined by the chosen variable ordering. A poor choice may cause an OBDD to be too large to fit into the available memory. The decision variant of the variable ordering problem is known to be -complete. We strengthen this result by showing that there in no polynomial time approximation scheme for the variable ordering problem unless . W...

Sample Method for Minimization of OBDDs

This paper contributes to the solution of the minimization problem of Ordered Binary Decision Diagrams by means of variable reordering. We suggest a new heuristic that is based on sampling. A small OBDD sample is chosen from the OBDDs that are considered for minimization. Solving the problem for this small sample, we obtain a variable order that is adapt for the entire OBDDs. We present rst exp...