Predicting Learnt Clauses Quality in Modern SAT Solvers

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Learning Optimal Decision Trees with SAT (2018, methodology): “The Glu-cose3 SAT solver [Audemard and Simon, 2009] was used, with a total timeout of 1000 sec in all experiments.”
Learning First-Order Symbolic Representations for Planning from the Structure of the State Space (2019, neutral): “1 [5].”
Core-Boosted Linear Search for Incomplete MaxSAT (2019, methodology): “All three algorithms were implemented on top of the publicly available Open-WBO system [40] using Glucose 4.1 [12] as the back-end SAT solver.”
Open-WBO: A Modular MaxSAT Solver, (2014, methodology): “0 [8,7], (v) ZENN [33], (vi) SINN [32], (vii) Glueminisat [27], (viii) GluH [29], (ix) glue bit [11], and (x) GlucoRed [31].”
Computing (2021, unknown): “Thus, the literature highlighted in this chapter, and the case study presented, is focused on furthering how we conceive of affective computing in the context of the built environment.”
Verifying Properties of Binarized Deep Neural Networks (2017, methodology): “The second method is a pure SAT method, based on our proposed encoding, where we used the Glucose SAT solver (Audemard and Simon 2009) to solve the resulting encoding (denoted SAT ).”
Improving Glucose for Incremental SAT Solving with Assumptions: Application to MUS Extraction (2013, methodology): “a good choice to use Glucose [14], a variant of Minisat [4] based on an efficient scoring mechanism of clauses, called Literal Block Distance (LBD).”