Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Separating the eraser Turing machine classes Le, NLe, co-NLe and Pe
Theoretical Computer Science
Size of ordered binary decision diagrams representing threshold functions
Theoretical Computer Science
Asymptotically optimal bounds for OBDDs and the solution of some basic OBDD problems
Journal of Computer and System Sciences
Computing strongly connected components in a linear number of symbolic steps
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Size and Variable Ordering of OBDDs Representing Treshold Functions
COCOON '97 Proceedings of the Third Annual International Conference on Computing and Combinatorics
The Effect of Null-Chains on the Complexity of Contact Schemes
FCT '89 Proceedings of the International Conference on Fundamentals of Computation Theory
On threshold BDDs and the optimal variable ordering problem
COCOA'07 Proceedings of the 1st international conference on Combinatorial optimization and applications
Exact OBDD bounds for some fundamental functions
SOFSEM'08 Proceedings of the 34th conference on Current trends in theory and practice of computer science
On symbolic scheduling independent tasks with restricted execution times
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Theoretical Computer Science
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Ordered binary decision diagrams (OBDDs) are one of the most common dynamic data structures for Boolean functions. Among the many areas of application are hardware verification, model checking, and symbolic graph algorithms. Threshold functions are the basic functions for discrete neural networks and are used as building blocks in the design of some symbolic graph algorithms. In this paper the first exponential lower bound on the size of a more general model than OBDDs and the first nontrivial asymptotically optimal bound on the OBDD size for a threshold function are presented.