Covered Clauses Are Not Propagation Redundant

Barnett, Lee A.; Cerna, David M.; Biere, Armin

doi:10.1007/978-3-030-51074-9_3

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…This potentially exploding reconstruction stack is a problem we also saw in the context of globally blocked clauses (Kiesl et al, 2019), also in practice, as well as for covered clause elimination (Barnett, Cerna, & Biere, 2020). We leave it to future work to come up with a space efficient method for autarky reconstruction in incremental SAT Solving.…”

Section: Model Reconstructionmentioning

confidence: 86%

Better Decision Heuristics in CDCL through Local Search and Target Phases

Zhang¹,

Fleury²,

Biere³

2022

jair

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On practical applications, state-of-the-art SAT solvers dominantly use the conflict-driven clause learning (CDCL) paradigm. An alternative for satisfiable instances is local search solvers, which is more successful on random and hard combinatorial instances. Although there have been attempts to combine these methods in one framework, a tight integration which improves the state of the art on a broad set of application instances has been missing. We present a combination of techniques that achieves such an improvement. Our first contribution is to maximize in a local search fashion the assignment trail in CDCL, by sticking to and extending promising assignments via a technique called target phases. Second, we relax the CDCL framework by again extending promising branches to complete assignments while ignoring conflicts. These assignments are then used as starting point of local search which tries to find improved assignments with fewer unsatisfied clauses. Third, these improved assignments are imported back to the CDCL loop where they are used to determine the value assigned to decision variables. Finally, the conflict frequency of variables in local search can be exploited during variable selection in branching heuristics of CDCL. We implemented these techniques to improve three representative CDCL solvers (Glucose, MapleLcm DistChronoBT, and Kissat). Experiments on benchmarks from the main tracks of the last three SAT Competitions from 2019 to 2021 and an additional benchmark set from spectrum allocation show that the techniques bring significant improvements, particularly and not surprisingly, on satisfiable real-world application instances. We claim that these techniques were essential to the large increase in performance witnessed in the SAT Competition 2020 where Kissat and Relaxed LcmdCbDl NewTech were leading the field followed by CryptoMiniSAT-Ccnr, which also incorporated similar ideas.

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Section: Model Reconstructionmentioning

confidence: 86%

Better Decision Heuristics in CDCL through Local Search and Target Phases

Zhang¹,

Fleury²,

Biere³

2022

jair

Self Cite

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“…Efficiently identifiable redundant clauses are at the foundation of many formula simplification techniques and refutation systems (for instance, see [32,33,37,43]). In general, deciding whether a clause is redundant is complete for the complement of the class DP [6], containing both NP and co-NP [55], so solvers and proof systems rely on polynomially-decidable redundancy properties for checking specific instances of redundancy. The following characterization of redundant clauses provides a common framework for formulating such properties.…”

Section: Introductionmentioning

confidence: 99%

Non-clausal Redundancy Properties

Barnett

Biere

2021

Automated Deduction – CADE 28

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State-of-the-art refutation systems for SAT are largely based on the derivation of clauses meeting some redundancy criteria, ensuring their addition to a formula does not alter its satisfiability. However, there are strong propositional reasoning techniques whose inferences are not easily expressed in such systems. This paper extends the redundancy framework beyond clauses to characterize redundancy for Boolean constraints in general. We show this characterization can be instantiated to develop efficiently checkable refutation systems using redundancy properties for Binary Decision Diagrams (BDDs). Using a form of reverse unit propagation over conjunctions of BDDs, these systems capture, for instance, Gaussian elimination reasoning over XOR constraints encoded in a formula, without the need for clausal translations or extension variables. Notably, these systems generalize those based on the strong Propagation Redundancy (PR) property, without an increase in complexity.

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CaDiCaL 2.0

Biere,

Faller,

Fazekas

et al. 2024

Computer Aided Verification

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The SAT solver CaDiCaL provides a rich feature set with a clean library interface. It has been adopted by many users, is well documented and easy to extend due to its effective testing and debugging infrastructure. In this tool paper we give a high-level introduction into the solver architecture and then go briefly over implemented techniques. We describe basic features and novel advanced usage scenarios. Experiments confirm that CaDiCaL despite this flexibility has state-of-the-art performance both in a stand-alone as well as incremental setting.

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Covered Clauses Are Not Propagation Redundant

Cited by 3 publications

References 25 publications

Better Decision Heuristics in CDCL through Local Search and Target Phases

Better Decision Heuristics in CDCL through Local Search and Target Phases

Non-clausal Redundancy Properties

CaDiCaL 2.0

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