scholarly journals Restrictive Acceptance Suffices for Equivalence Problems

2000 ◽  
Vol 3 ◽  
pp. 86-95 ◽  
Author(s):  
Bernd Borchert ◽  
Lane A. Hemaspaandra ◽  
Jörg Rothe

AbstractOne way of suggesting that an NP problem may not be NP-complete is to show that it is in the promise class UP. We propose an analogous new method—weaker in strength of evidence but more broadly applicable—for suggesting that concrete NP problems are not NP-complete. In particular, we introduce the promise class EP, the subclass of NP consisting of those languages accepted by NP machines that, when they accept, always have a number of accepting paths that is a power of two. We show that FewP, bounded ambiguity polynomial time (which contains UP), is contained in EP. The class EP applies as an upper bound to some concrete problems to which previous approaches have never been successful, for example the negation equivalence problem for OBDDs (ordered binary decision diagrams).

1993 ◽  
Vol 03 (01) ◽  
pp. 3-12 ◽  
Author(s):  
DETLEF SIELING ◽  
INGO WEGENER

(Ordered) binary decision diagrams are a powerful representation for Boolean functions and are widely used in logical synthesis, verification, test pattern generation or as part of CAD tools. NC-algorithms are presented for the most important operations on this representation, e.g. evaluation for a given input, minimization, satisfiability, redundancy test, replacement of variables by constants or functions, equivalence test and synthesis. The algorithms have logarithmic run time on CRCW COMMON PRAMs with a polynomial number of processors.


2000 ◽  
Vol 103 (1-3) ◽  
pp. 237-258 ◽  
Author(s):  
Martin Sauerhoff ◽  
Ingo Wegener ◽  
Ralph Werchner

Author(s):  
Anna Louise D. Latour ◽  
Behrouz Babaki ◽  
Siegfried Nijssen

A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature.


1999 ◽  
Vol 09 (03n04) ◽  
pp. 181-198 ◽  
Author(s):  
CHRISTOPH MEINEL ◽  
THORSTEN THEOBALD

Many problems in computer-aided design of highly integrated circuits (CAD for VLSI) can be transformed to the task of manipulating objects over finite domains. The efficiency of these operations depends substantially on the chosen data structures. In the last years, ordered binary decision diagrams (OBDDs) have proven to be a very efficient data structure in this context. Here, we give a survey on these developments and stress the deep interactions between basic research and practically relevant applied research with its immediate impact on the performance improvement of modern CAD design and verification tools.


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