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Title: Towards a Complexity-Theoretic Understanding of Restarts in SAT Solvers
Restarts are a widely-used class of techniques integral to the efficiency of Conflict-Driven Clause Learning (CDCL) Boolean SAT solvers. While the utility of such policies has been well-established empirically, a theoretical understanding of whether restarts are indeed crucial to the power of CDCL solvers is missing. In this paper, we prove a series of theoretical results that characterize the power of restarts for various models of SAT solvers. More precisely, we make the following contributions. First, we prove an exponential separation between a drunk randomized CDCL solver model with restarts and the same model without restarts using a family of satisfiable instances. Second, we show that the configuration of CDCL solver with VSIDS branching and restarts (with activities erased after restarts) is exponentially more powerful than the same configuration without restarts for a family of unsatisfiable instances. To the best of our knowledge, these are the first separation results involving restarts in the context of SAT solvers. Third, we show that restarts do not add any proof complexity-theoretic power vis-a-vis a number of models of CDCL and DPLL solvers with non-deterministic static variable and value selection.
Authors:
; ; ; ;
Award ID(s):
1900460
Publication Date:
NSF-PAR ID:
10169751
Journal Name:
SAT 2020: International Conference on Theory and Applications of Satisfiability Testing
Volume:
12178
Page Range or eLocation-ID:
233 - 249
Sponsoring Org:
National Science Foundation
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