Quasi-Exact BDD Minimization Using Relaxed Best-First Search

In this paper we present a new method for quasiexact optimization of BDDs using relaxed ordered best-first search. This general method is applied to BDD minimization. In contrast to a known relaxation of A∗ , the new method guarantees to expand every state exactly once if guided by a monotone heuristic function. By that, it effectively accounts for aspects of run time while still guaranteeing that the cost of the solution will not exceed the optimal cost by a factor greater than (1 + ) n 2 where n is the maximal length of a solution path. E.g., for 25 BDD variables and using a degree of relaxation of 5%, the BDD size

• Type: Conference
• Year: 2005
• Where: ISVLSI
• Authors: Rüdiger Ebendt, Rolf Drechsler
• Keywords: General Method, ISVLSI 2005, Monotone Heuristic Function, Quasiexact Optimization