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Sorting Parity Encodings by Reusing Variables
Parity reasoning is challenging for CDCL solvers: Refuting a formula consisting of two contradictory, differently ordered parity constraints of modest size is hard. Two alternative methods can solve these reordered parity formulas efficiently: binary decision diagrams and Gaussian Elimination (which...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326471/ http://dx.doi.org/10.1007/978-3-030-51825-7_1 |
Sumario: | Parity reasoning is challenging for CDCL solvers: Refuting a formula consisting of two contradictory, differently ordered parity constraints of modest size is hard. Two alternative methods can solve these reordered parity formulas efficiently: binary decision diagrams and Gaussian Elimination (which requires detection of the parity constraints). Yet, implementations of these techniques either lack support of proof logging or introduce many extension variables. The compact, commonly-used encoding of parity constraints uses Tseitin variables. We present a technique for short clausal proofs that exploits these Tseitin variables to reorder the constraints within the DRAT system. The size of our refutations of reordered parity formulas is [Formula: see text]. |
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