Representative Encodings to Translate Finite CSPs into SAT

Barahona, Pedro; Hölldobler, Steffen; Nguyen, Van-Hau

doi:10.1007/978-3-319-07046-9_18

Cited by 8 publications

(12 citation statements)

References 17 publications

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“…Further proposed encodings for the AtMost-1 constraint are the log encoding [33], the ladder encoding [18,17] also defined independently in [3], the commander encoding [23], generalizations of the log encoding and the two-product encoding [14], the bimander encoding [20], as well as generalizations of the bimander encoding [7].…”

Section: Short Review Of Known Encodingsmentioning

confidence: 99%

Detecting Cardinality Constraints in CNF

Biere

Berre²,

Lonca³

et al. 2014

Lecture Notes in Computer Science

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Abstract. We present novel approaches to detect cardinality constraints expressed in CNF. The first approach is based on a syntactic analysis of specific data structures used in SAT solvers to represent binary and ternary clauses, whereas the second approach is based on a semantic analysis by unit propagation. The syntactic approach computes an approximation of the cardinality constraints AtMost-1 and AtMost-2 constraints very fast, whereas the semantic approach has the property to be generic, i.e. it can detect cardinality constraints AtMost-k for any k, at a higher computation cost. Our experimental results suggest that both approaches are efficient at recovering AtMost-1 and AtMost-2 cardinality constraints.

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Section: Short Review Of Known Encodingsmentioning

confidence: 99%

Detecting Cardinality Constraints in CNF

Biere

Berre²,

Lonca³

et al. 2014

Lecture Notes in Computer Science

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“…Recently, Barahona et al have introduced two SAT-encoded CSPs [13]: the representative-sparse and representative-order encoding (we say the representative encodings). The representative encodings aim at not only taking advantage of the sparse and order encodings but also requiring a significantly smaller number of SAT variables.…”

Section: The Representative Encodingmentioning

confidence: 99%

“…On the other hand, there is only one approach for SAT to deal with this constraint by posing the at-leastone (ALO) and at-most-one (AMO) clauses (see [33,11,34]). In fact, the state-of-the-art SAT solvers need more than one hour for tackling the pigeon-hole problem with only 16 holes [49,13].…”

Section: Introductionmentioning

confidence: 99%

Solving the all-interval series problem

Nguyen

Son

2014

Proceedings of the Fifth Symposium on Information and Communication Technology - SoICT '14

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Although Boolean satisfiability (abbreviated as SAT) is a sub-field of constraint programming (CP), the former states and solves problems as a black-box approach, whereas the latter aims at being tunable and programmable. Although many researches bridging SAT and CP have been provided, surprisingly, only few researchers have compared the SAT and CP approaches on a particular problem. This paper studies how to solve the all-interval series problem through both approaches. We will show that by using a state-of-theart SAT solver and an appropriate SAT encoding the SAT approach obtains a significantly higher performance over the CP approach. Furthermore, we also provide the state-of-theart result for several all-interval series instances.

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“…There exists a lot of work proposing different techniques for encoding a CSP into a SAT problem [4,104,18,178,174]. A hybrid approach, called Lazy Clause Generation, is instead presented in [149].…”

Section: Nogood Learning and Lazy Clause Generationmentioning

confidence: 99%

“…In more detail, mzn2feat-1.0 extracts in total 95 features. The variables (27), domains (18), constraints (27), and solving (11) features are exactly the same of mzn2feat (see Table 4 Finally, we compared sunny-tps vs. the following approaches:…”

Section: Empirical Evaluationmentioning

confidence: 99%

Portfolio approaches in constraint programming

Amadini

2015

Constraints

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Recent research has shown that the performance of a single, arbitrarily efficient algorithm can be significantly outperformed by using a portfolio of -possibly onaverage slower-algorithms. Within the Constraint Programming (CP) context, a portfolio solver can be seen as a particular constraint solver that exploits the synergy between the constituent solvers of its portfolio for predicting which is (or which are) the best solver(s) to run for solving a new, unseen instance.In this thesis we examine the benefits of portfolio solvers in CP. Despite portfolio approaches have been extensively studied for Boolean Satisfiability (SAT) problems, in the more general CP field these techniques have been only marginally studied and used. We conducted this work through the investigation, the analysis and the construction of several portfolio approaches for solving both satisfaction and optimization problems. We focused in particular on sequential approaches, i.e., single-threaded portfolio solvers always running on the same core.We started from a first empirical evaluation on portfolio approaches for solving Constraint Satisfaction Problems (CSPs), and then we improved on it by introducing new data, solvers, features, algorithms, and tools. Afterwards, we addressed the more general Constraint Optimization Problems (COPs) by implementing and testing a number of models for dealing with COP portfolio solvers. Finally, we have come full circle by developing sunny-cp: a sequential CP portfolio solver that turned out to be competitive also in the MiniZinc Challenge, the reference competition for CP solvers.iii iv

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Representative Encodings to Translate Finite CSPs into SAT

Cited by 8 publications

References 17 publications

Detecting Cardinality Constraints in CNF

Detecting Cardinality Constraints in CNF

Solving the all-interval series problem

Portfolio approaches in constraint programming

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