Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Finding the Optimal Variable Ordering for Binary Decision Diagrams
IEEE Transactions on Computers
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
Interleaving based variable ordering methods for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Dynamic variable ordering for ordered binary decision diagrams
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Wave steering in YADDs: a novel non-iterative synthesis and layout technique
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
On-the-fly layout generation for PTL macrocells
Proceedings of the conference on Design, automation and test in Europe
The nonapproximability of OBDD minimization
Information and Computation
Automation and Remote Control
Combining ordered best-first search with branch and bound for exact BDD minimization
Proceedings of the 2004 Asia and South Pacific Design Automation Conference
BDS: a BDD-based logic optimization system
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
An improved branch and bound algorithm for exact BDD minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Combining ordered best-first search with branch and bound for exact BDD minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A framework for quasi-exact optimization using relaxed best-first search
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
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In this paper we present a new method for quasi-exact 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 + \varepsilon )^{\left\lfloor {\frac{n}{2}} \right\rfloor } 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 is guaranteed to be not greater than 1.8 times the optimal size. Within a range of reasonable choices for \varepsilon, the method allows the user to trade off run time for solution quality. Experimental results demonstrate large reductions in run time when compared to the best known exact approach. Moreover, the quality of the obtained solutions is much better than the quality guaranteed by the theory.