QCDCL with cube learning or pure literal elimination – What is best?

Quantified conflict-driven clause learning (QCDCL) is one of the main approaches for solving quantified Boolean formulas (QBF). We formalise and investigate several versions of QCDCL that include cube learning and/or pure-literal elimination, and formally compare the resulting solving variants via proof complexity techniques. Our results show that almost all of the QCDCL variants are exponentially incomparable with respect to proof size (and hence solver running time), pointing towards different orthogonal ways how to practically implement QCDCL.

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Publication Details

Journal
Artificial Intelligence
Published
2024-08-08
DOI
https://doi.org/10.1016/j.artint.2024.104194
Citations
1
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
0.35

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article

QCDCL with cube learning or pure literal elimination – What is best?

Olaf Beyersdorff, Tomáš Peitl, Benjamin Böhm
1 citations
Artificial Intelligence
Algorithms and Data Compression
0.35
article

QCDCL with cube learning or pure literal elimination – What is best?

Olaf Beyersdorff, Tomáš Peitl, Benjamin Böhm
article en
1 citations

Abstract

Quantified conflict-driven clause learning (QCDCL) is one of the main approaches for solving quantified Boolean formulas (QBF). We formalise and investigate several versions of QCDCL that include cube learning and/or pure-literal elimination, and formally compare the resulting solving variants via proof complexity techniques. Our results show that almost all of the QCDCL variants are exponentially incomparable with respect to proof size (and hence solver running time), pointing towards different orthogonal ways how to practically implement QCDCL.

Artificial IntelligenceVol. 336
TU Wien (AT), Friedrich Schiller University Jena (DE)
Carl-Zeiss-Stiftung, Deutsche Forschungsgemeinschaft, Austrian Science Fund
Openalex Percentile: Top 35%
Algorithms and Data Compression
0.35
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