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Approximation and decomposition of binary decision diagrams
Proceedings of the 35th annual conference on Design automation conference - DAC '98, 1998Efficient techniques for the manipulation of Binary Decision Diagrams (BDDs) are key to the success of formal verification tools. Recent advances in reachability analysis and model checking algorithms have emphasized the need for efficient algorithms for the approximation and decomposition of BDDs.
Kavita Ravi +3 more
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Binary decision diagrams and integer programming
2007In dieser Arbeit zeigen wir, wie Binary Decision Diagrams (BDDs) als ein mächtiges Werkzeug für die 0/1 Ganzzahlige Programmierung (0/1 IP) und zugehörige polyedrische Probleme eingesetzt werden können. Wir entwickeln einen output-sensitiven Algorithmus zum Bauen eines Threshold BDDs, der die zulässigen 0/1 Lösungen einer linearen Ungleichung darstellt,
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Binary decision diagrams on network of workstations
Proceedings International Conference on Computer Design. VLSI in Computers and Processors, 2002The success of all binary decision diagram (BDD) based synthesis and verification algorithms depend on the ability to efficiently manipulate very large BDDs. We present algorithms for manipulation of very large Binary Decision Diagrams (BDDs) on a network of workstations (NOW).
Rajeev K. Ranjan 0001 +3 more
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Synthesis for Testability: Binary Decision Diagrams
1992We investigate the testability properties of Boolean circuits derived from (Reduced Ordered) Binary Decision Diagrams. It is shown that BDD-cirucits (or at least) BDD-like circuits are easily testable with respect to different fault models (cellular, stuck-at and path delay fault model).
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Comments on "A characterization of binary decision diagrams"
IEEE Transactions on Computers, 1994Chakravarty presents a characterization of BDD's in terms of the complexity of some computational problems, ibid., vol. 42, p. 129-137, Feb. 1993. In these comments, some incorrectly stated restrictions on the "number of repeated variables" are corrected and results on the translation problem (to include EXOR and NEXOR gates) are generalized. >
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Distributed binary decision diagrams for symbolic reachability
Proceedings of the 24th ACM SIGSOFT International SPIN Symposium on Model Checking of Software, 2017Decision diagrams are used in symbolic verification to concisely represent state spaces. A crucial symbolic verification algorithm is reachability: systematically exploring all reachable system states. Although both parallel and distributed reachability algorithms exist, a combined solution is relatively unexplored.
Wytse Oortwijn +2 more
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Binary superposed quantum decision diagrams
Quantum Information Processing, 2009The key theme of this work is the binary superposed decision diagrams or BSQDDs for short. The basic idea that lies behind BSQDDs is to represent a quantum superposition as a decision diagram where each node on each branch of a BSQDD corresponds to a gate.
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2018
Binary decision diagrams provide a data structure for representing and manipulating Boolean functions in symbolic form. They have been especially effective as the algorithmic basis for symbolic model checkers. A binary decision diagram represents a Boolean function as a directed acyclic graph, corresponding to a compressed form of decision tree.
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Binary decision diagrams provide a data structure for representing and manipulating Boolean functions in symbolic form. They have been especially effective as the algorithmic basis for symbolic model checkers. A binary decision diagram represents a Boolean function as a directed acyclic graph, corresponding to a compressed form of decision tree.
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2011
A binary decision diagram is a directed acyclic graph that consists of nodes and edges. It deals with Boolean functions. A binary decision diagram consists of a set of decision nodes, starting at the root node at the top of the decision diagram. Each decision node contains two outgoing branches, one is a high branch and the other is a low branch. These
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A binary decision diagram is a directed acyclic graph that consists of nodes and edges. It deals with Boolean functions. A binary decision diagram consists of a set of decision nodes, starting at the root node at the top of the decision diagram. Each decision node contains two outgoing branches, one is a high branch and the other is a low branch. These
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On the Minimization of (Complete) Ordered Binary Decision Diagrams
Theory of Computing Systems, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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