sharpSAT – Counting Models with Advanced Component Caching and Implicit BCP

Thurley, Marc

doi:10.1007/11814948_38

Cited by 168 publications

(153 citation statements)

References 5 publications

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“…Fig. 3 shows the distribution of α and the number of solutions for these blocks (computed using sharpSAT [34]), highlighting a challenge in constructing efficient blocked filters. In order to ensure that almost all blocks are satisfiable, the number of blocks must be high enough that almost the entire binomial distribution of α is below the satisfiability threshold α s .…”

Section: Case Study 1: a Blocked Mnae3sat Filtermentioning

confidence: 99%

Constructing SAT Filters with a Quantum Annealer

Douglass

King

Raymond

2015

Lecture Notes in Computer Science

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Abstract. SAT filters are a novel and compact data structure that can be used to quickly query a word for membership in a fixed set. They have the potential to store more information in a fixed storage limit than a Bloom filter. Constructing a SAT filter requires sampling diverse solutions to randomly constructed constraint satisfaction instances, but there is flexibility in the choice of constraint satisfaction problem. Presented here is a case study of SAT filter construction with a focus on constraint satisfaction problems based on MAX-CUT clauses (Not-all-equal 3-SAT, 2-in-4-SAT, etc.) and frustrated cycles in the Ising model. Solutions are sampled using a D-Wave quantum annealer, and results are measured against classical approaches. The SAT variants studied are of interest in the context of SAT filters, independent of the solvers used.

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Section: Case Study 1: a Blocked Mnae3sat Filtermentioning

confidence: 99%

Constructing SAT Filters with a Quantum Annealer

Douglass

King

Raymond

2015

Lecture Notes in Computer Science

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“…More specifically, we exploit the exhaustive search performed by the #SAT solver, sharpSAT [10]. The exhaustive DPLL algorithm is a modification of DPLL used to find all solutions and, therefore, to implicitly explore the entire search space.…”

Section: Preliminariesmentioning

confidence: 99%

“…Implicit Binary Constraint Propagation DSHARP employs a simple form of lookahead during search called implicit binary constraint propagation (IBCP) [10]. In IBCP, a subset of the unassigned variables are heuristically chosen at a decision node and the impact of assigning any one of them is evaluated.…”

Section: Dsharpmentioning

confidence: 99%

“…4 Our compiler builds on the research by Huang and Darwiche showing that d-DNNF can be extracted from the trace of an exhaustive search of a propositional theory [4]. To this end, we construct our compiler by appealing to a state-of-the-art #SAT solver, sharpSAT [10]. Our compiler exploits two significant features of sharpSAT that distinguish it from previous CNF → d-DNNF compilers: dynamic decomposition and implicit binary constraint propagation.…”

Section: Introductionmentioning

confidence: 99%

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Dsharp: Fast d-DNNF Compilation with sharpSAT

Muise

McIlraith

Beck

et al. 2012

Advances in Artificial Intelligence

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Abstract. Knowledge compilation is a compelling technique for dealing with the intractability of propositional reasoning. One particularly effective target language is Deterministic Decomposable Negation Normal Form (d-DNNF). We exploit recent advances in #SAT solving in order to produce a new state-ofthe-art CNF → d-DNNF compiler: DSHARP. Empirical results demonstrate that DSHARP is generally an order of magnitude faster than C2D, the de facto standard for compiling to d-DNNF, while yielding a representation of comparable size.

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“…We have extended two such tools 5 , sharpSAT [25] and Dsharp [20], with projection capabilities-something that has not been available in # Figure 3 illustrates such a decision on variable x. The # SAT solvers also employ a number of optimizations (e.g., subtree caching, clause learning, etc.…”

Section: Model Counting Via Compilation To D-dnnfmentioning

confidence: 99%

SAT-Based Analysis and Quantification of Information Flow in Programs

Klebanov

Manthey

Muise

2013

Quantitative Evaluation of Systems

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Abstract. Quantitative information flow analysis (QIF) is a portfolio of security techniques quantifying the flow of confidential information to public ports. In this paper, we advance the state of the art in QIF for imperative programs. We present both an abstract formulation of the analysis in terms of verification condition generation, logical projection and model counting, and an efficient concrete implementation targeting ANSI C programs. The implementation combines various novel and existing SAT-based tools for bounded model checking, # SAT solving in presence of projection, and SAT preprocessing. We evaluate the technique on synthetic and semi-realistic benchmarks.

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sharpSAT – Counting Models with Advanced Component Caching and Implicit BCP

Cited by 168 publications

References 5 publications

Constructing SAT Filters with a Quantum Annealer

Constructing SAT Filters with a Quantum Annealer

Dsharp: Fast d-DNNF Compilation with sharpSAT

SAT-Based Analysis and Quantification of Information Flow in Programs

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