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Biere, Bernd Ulrich

doi:10.1515/9783111631844

Cited by 39 publications

(13 citation statements)

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“…To experiment with our SAT-based implementation and for further details on our experiments we refer to http://aprove.informatik.rwth-aachen.de/eval/SATDP/. There, we also provide data on experiments with other (DPLL-based) SAT solvers like glucose [2], PrecoSAT [6], and MiniSAT2 [13]. These experiments indicate that the choice of the SAT solver does not have a significant influence on the overall performance of the termination prover.…”

Section: Discussionmentioning

confidence: 99%

SAT Solving for Termination Proofs with Recursive Path Orders and Dependency Pairs

et al. 2010

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This paper introduces a propositional encoding for recursive path orders (RPO), in connection with dependency pairs. Hence, we capture in a uniform setting all common instances of RPO, i.e., lexicographic path orders (LPO), multiset path orders (MPO), and lexicographic path orders with status (LPOS). This facilitates the application of SAT solvers for termination analysis of term rewrite systems (TRSs).We address four main inter-related issues and show how to encode them as satisfiability problems of propositional formulas that can be efficiently handled by SAT solving: (A) the lexicographic comparison w.r.t. a permutation of the arguments; (B) the multiset extension of a base order; (C) the combined search for a path order together with an argument filter to orient a set of inequalities; and (D) how the choice of the argument filter influences the set of inequalities that have to be oriented (so-called usable rules).We have implemented our contributions in the termination prover AProVE. Extensive experiments show that by our encoding and the application of SAT solvers one obtains speedups in orders of magnitude as well as increased termination proving power.

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Section: Discussionmentioning

confidence: 99%

SAT Solving for Termination Proofs with Recursive Path Orders and Dependency Pairs

et al. 2010

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“…As the optimization counterpart of the Boolean satisfiability (SAT) problem, the MAX-SAT problem is one of well-studied NP-hard optimization problems [13]. The Max-SAT problem can be described as quadruple (X, C, W, W c ).…”

Section: Max-sat Problemmentioning

confidence: 99%

An Backbone Guided Extremal Optimization Method for Solving the Hard Maximum Satisfiability Problem

Zeng¹,

Chen

Zhang³

et al. 2012

Proceedings of the 2nd International Conference on Computer Application and System Modeling

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Abstract-The original Extremal Optimization (EO) algorithmand its modified versions have been successfully applied to a variety of NP-hard optimization problems. However, almost all existing EO-based algorithms have overlooked the inherent structural properties behind the optimization problems, e.g., the backbone information. This paper presents a novel stochastic local search method called Backbone Guided Extremal Optimization (BGEO) to solve the hard maximum satisfiability (MAX-SAT) problem, one of typical NP-hard problems. The key idea behind the proposed method is to incorporate the backbone information into a recent developed optimization algorithm termed extremal optimization (EO) to guide the entire search process approach the optimal solutions. The superiority of BGEO to the reported BE-EEO algorithm without backbone information is demonstrated by the experimental results on the hard Max-SAT instances.

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“…4 ) The dialogical approach is mainly represented by authors with a background from linguistics and/or discourse analysis (cf. Heringer 1979, Muckenhaupt 1981, Hoffmann 1984, Biere 1989. Some authors, like van Dijk, try to get the best of both worlds (cf.…”

Section: Dialogical Approach and Cognitive Approachmentioning

confidence: 99%