On Freezing and Reactivating Learnt Clauses

Audemard, Gilles; Lagniez, Jean-Marie; Mazure, Bertrand; Saïs, Lakhdar

doi:10.1007/978-3-642-21581-0_16

Cited by 36 publications

(35 citation statements)

References 13 publications

(16 reference statements)

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“…less useful learned [21,22] clauses are frequently collected during the main CDCL loop of the SAT solver anyhow, many abbreviations turn out not to be referenced anymore after a certain point. They become garbage abbreviations and could be collected too.…”

Section: Assigning the Set Of Necessary Abbreviationsmentioning

confidence: 99%

“…Thus heuristics to determine which learned clauses to keep resp. how and when to reduce the learned clause database are an essential part of state-of-the-art SAT solvers [20,21,22]. After an incremental SAT call returned "unsatisfiable", we propose to only keep those learned clauses, which were used to show that the assumed assumptions in this SAT call are inconsistent and discard all others.…”

Section: Reduce Learned Clause Databasementioning

confidence: 99%

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Factoring Out Assumptions to Speed Up MUS Extraction

Lagniez

Biere

2013

Lecture Notes in Computer Science

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Abstract. In earlier work on a limited form of extended resolution for CDCL based SAT solving, new literals were introduced to factor out parts of learned clauses. The main goal was to shorten clauses, reduce proof size and memory usage and thus speed up propagation and conflict analysis. Even though some reduction was achieved, the effectiveness of this technique was rather modest for generic SAT solving. In this paper we show that factoring out literals is particularly useful for incremental SAT solving, based on assumptions. This is the most common approach for incremental SAT solving and was pioneered by the authors of MINISAT. Our first contribution is to focus on factoring out only assumptions, and actually all eagerly. This enables the use of compact dedicated data structures, and naturally suggests a new form of clause minimization, our second contribution. As last main contribution, we propose to use these data structures to maintain a partial proof trace for learned clauses with assumptions, which gives us a cheap way to flush useless learned clauses. In order to evaluate the effectiveness of our techniques we implemented them within the version of MINISAT used in the publically available state-of-the-art MUS extractor MUSer. An extensive experimental evaluation shows that factoring out assumptions in combination with our novel clause minimization procedure and eager clause removal is particularly effective in reducing average clause size, improves running time and in general the state-of-the-art in MUS extraction.

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Section: Assigning the Set Of Necessary Abbreviationsmentioning

confidence: 99%

Section: Reduce Learned Clause Databasementioning

confidence: 99%

Factoring Out Assumptions to Speed Up MUS Extraction

Lagniez

Biere

2013

Lecture Notes in Computer Science

Self Cite

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“…al suggest to "freeze" learned clauses if they have not been used for while, instead of deleting them immediately [4]. In a parallel setting, they lift this approach to received clauses [8].…”

Section: Handling Received Clausesmentioning

confidence: 99%

Tuning Parallel SAT Solvers

Ehlers¹,

Nowotka²

2018

EasyChair Preprints

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In this paper we present new implementation details and benchmarking results for our parallel portfolio solver TopoSAT2. In particular, we discuss ideas and implementation details for the exchange of learned clauses in a massively-parallel SAT solver which is designed to run more that 1, 000 solver threads in parallel. Furthermore, we go back to the roots of portfolio SAT solving, and discuss the impact of diversifying the solver by using different restart-, branching-and clause database management heuristics. We show that these techniques can be used to tune the solver towards different problems. However, in a case study on formulas derived from Bounded Model Checking problems we see the best performance when using a rather simple clause exchange strategy. We show details of these tests and discuss possible explanations for this phenomenon.As computing times on massively-parallel clusters are expensive, we consider it especially interesting to share these kind of experimental results.

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“…One of the main reasons for this efficiency leap was the extension of the conflict-driven clause learning (CDCL) algorithm [38] by a number of inprocessing techniques [27], such as clause elimination [12,3,2,26], variable elimination [46,11], bounded variable addition [33], cardinality resolution [6], symmetry breaking [8,1] and parity reasoning [44,45,31,32]. These techniques modify the CNF formula in the SAT solver and replace it by a satisfiability-equivalent one, and often semantic equivalence is not preserved.…”

Section: Introductionmentioning

confidence: 99%

Towards a Semantics of Unsatisfiability Proofs with Inprocessing

Philipp

Rebola-Pardo

EPiC Series in Computing

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Delete Resolution Asymmetric Tautology (DRAT) proofs have become a de facto standard to certify unsatisfiability results from SAT solvers with inprocessing. However, DRAT shows behaviors notably different from other proof systems: DRAT inferences are nonmonotonic, and clauses that are not consequences of the premises can be derived. In this paper, we clarify some discrepancies on the notions of reverse unit propagation (RUP) clauses and asymmetric tautologies (AT), and furthermore develop the concept of resolution consequences. This allows us to present an intuitive explanation of RAT in terms of permissive definitions. We prove that a formula derived using RATs can be stratified into clause sets depending on which definitions they require, which give a strong invariant along RAT proofs. We furthermore study its interaction with clause deletion, characterizing DRAT derivability as satisfiability-preservation.

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On Freezing and Reactivating Learnt Clauses

Cited by 36 publications

References 13 publications

Factoring Out Assumptions to Speed Up MUS Extraction

Factoring Out Assumptions to Speed Up MUS Extraction

Tuning Parallel SAT Solvers

Towards a Semantics of Unsatisfiability Proofs with Inprocessing

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