Work

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

Year: 2024

Type: article

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.

Source: Artificial Intelligence

DOI: 10.1016/j.artint.2024.104194

Authors: Benjamin Böhm, Tomáš Peitl, Olaf Beyersdorff

Institutions: Friedrich Schiller University Jena, TU Wien

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,070

Funders: Carl-Zeiss-Stiftung, Deutsche Forschungsgemeinschaft, Austrian Science Fund