Limits of CDCL Learning via Merge Resolution

التفاصيل البيبلوغرافية
العنوان: Limits of CDCL Learning via Merge Resolution
المؤلفون: Vinyals, Marc, Li, Chunxiao, Fleming, Noah, Kolokolova, Antonina, Ganesh, Vijay
سنة النشر: 2023
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Computational Complexity, Computer Science - Logic in Computer Science, 03F20, F.2.2
الوصف: In their seminal work, Atserias et al. and independently Pipatsrisawat and Darwiche in 2009 showed that CDCL solvers can simulate resolution proofs with polynomial overhead. However, previous work does not address the tightness of the simulation, i.e., the question of how large this overhead needs to be. In this paper, we address this question by focusing on an important property of proofs generated by CDCL solvers that employ standard learning schemes, namely that the derivation of a learned clause has at least one inference where a literal appears in both premises (aka, a merge literal). Specifically, we show that proofs of this kind can simulate resolution proofs with at most a linear overhead, but there also exist formulas where such overhead is necessary or, more precisely, that there exist formulas with resolution proofs of linear length that require quadratic CDCL proofs.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/2304.09422
رقم الأكسشن: edsarx.2304.09422
قاعدة البيانات: arXiv