Truth Assignments as Conditional Autarkies

Kiesl, Benjamin; Heule, Marijn J. H.; Biere, Armin

doi:10.1007/978-3-030-31784-3_3

Cited by 5 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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: 87%

See 1 more Smart Citation

Better Decision Heuristics in CDCL through Local Search and Target Phases

Zhang¹,

Fleury²,

Biere³

2022

jair

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Model Reconstructionmentioning

confidence: 87%

“…We use an algorithm originally proposed by Kullmann and described in a publication by Kiesl et al (Kiesl, Heule, & Biere, 2019). Instead of forcing the autark assignment we simply eliminate the clauses touched by the autarky as well as the variables assigned by it.…”

Section: Algorithmmentioning

confidence: 99%

Better Decision Heuristics in CDCL through Local Search and Target Phases

Zhang¹,

Fleury²,

Biere³

2022

jair

Self Cite

View full text Add to dashboard Cite

show abstract

Covered Clauses Are Not Propagation Redundant

Barnett

Cerna

Biere

2020

Automated Reasoning

View full text Add to dashboard Cite

CaDiCaL 2.0

Biere,

Faller,

Fazekas

et al. 2024

Computer Aided Verification

View full text Add to dashboard Cite

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.

show abstract

Truth Assignments as Conditional Autarkies

Cited by 5 publications

References 31 publications

Better Decision Heuristics in CDCL through Local Search and Target Phases

Better Decision Heuristics in CDCL through Local Search and Target Phases

Covered Clauses Are Not Propagation Redundant

CaDiCaL 2.0

Contact Info

Product

Resources

About