Revisiting Hyper Binary Resolution

Heule, Marijn J. H.; Järvisalo, Matti; Biere, Armin

doi:10.1007/978-3-642-38171-3_6

Cited by 8 publications

(4 citation statements)

References 22 publications

(31 reference statements)

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“…Furthermore, the fact that cardinality detection is performed throughout the search means that the solver can also discover and exploit cardinality constraints that were not there in the original input, but have arisen as a result of the constraints learned during search. Recovery of short clauses during CDCL search has been considered before (Han, Jin, and Somenzi 2011;Heule, Järvisalo, and Biere 2013). However, our work differs from these papers in that they detect only binary clauses whereas we find also longer (though still comparatively short) clauses, and in that we use the found clauses to form syntactic cardinality constraints.…”

Section: Our Contributionmentioning

confidence: 76%

A Cardinal Improvement to Pseudo-Boolean Solving

Elffers

Nordstrm

2020

AAAI

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Pseudo-Boolean solvers hold out the theoretical potential of exponential improvements over conflict-driven clause learning (CDCL) SAT solvers, but in practice perform very poorly if the input is given in the standard conjunctive normal form (CNF) format. We present a technique to remedy this problem by recovering cardinality constraints from CNF on the fly during search. This is done by collecting potential building blocks of cardinality constraints during propagation and combining these blocks during conflict analysis. Our implementation has a non-negligible but manageable overhead when detection is not successful, and yields significant gains for some SAT competition and crafted benchmarks for which pseudo-Boolean reasoning is stronger than CDCL. It also boosts performance for some native pseudo-Boolean formulas where this approach helps to improve learned constraints.

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Section: Our Contributionmentioning

confidence: 76%

A Cardinal Improvement to Pseudo-Boolean Solving

Elffers

Nordstrm

2020

AAAI

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“…∨ p a k ) be a blocking clause. According to the constraint (1), each item a i is involved in many negative binary clauses of the form: Now, one of the most known form of resolution called hyper binary resolution [17] can be applied between C and the previous set of negative binary clauses clauses. This gives us the following general constraint of the form:…”

Section: Sat-based Approach For Efficient Mfi Miningmentioning

confidence: 99%

On Maximal Frequent Itemsets Mining with Constraints

Jabbour

Mana

Dlala

et al. 2018

Lecture Notes in Computer Science

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Recently, a new declarative mining framework based on constraint programming (CP) and propositional satisfiability (SAT) has been designed to deal with several pattern mining tasks. The itemset mining problem has been modeled using constraints whose models correspond to the patterns to be mined. In this paper, we propose a new propositional satisfiability based approach for mining maximal frequent itemsets that extends the one proposed in [20]. We show that instead of adding constraints to the initial SAT based itemset mining encoding, the maximal itemsets can be obtained by performing clause learning during search. A major strength of our approach rises in the compactness of the proposed encoding and the efficiency of the SAT-based maximal itemsets enumeration derived using blocked clauses. Experimental results on several datasets, show the feasibility and the efficiency of our approach.

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“…Unit propagation is a common basis for many different simplification techniques [3,12,13,17,18,20,21,31,32,35,38]. A key example is failed literal elimination [13,31,34], which aims at deducing unit clauses via checking whether assuming a truth value for a single variable results in a conflict by unit propagation.…”

Section: Introductionmentioning

confidence: 99%