TY - GEN
T1 - Using Resolution Proofs to Analyse CDCL Solvers
AU - Kokkala, Janne I.
AU - Nordström, Jakob
PY - 2020
Y1 - 2020
N2 - We propose that CDCL SAT solver heuristics such as restarts and clause database management can be analysed by studying the resolution proofs produced by the solvers, and by trimming these proofs to extract the clauses actually used to reach the final conclusion. We find that for non-adaptive Luby restarts higher frequency makes both untrimmed and trimmed proofs smaller, while adaptive restarts based on literal block distance (LBD) decrease proof size further mainly for untrimmed proofs. This seems to indicate that restarts improve the reasoning power of solvers, but that making restarts adaptive mainly helps to avoid useless work that is not needed to reach the end result. For clause database management we find that switching off clause erasures often, though not always, leads to smaller untrimmed proofs, but has no significant effect on trimmed proofs. With respect to quality measures for learned clauses, activity in conflict analysis is a fairly good predictor in general for a clause ending up also in the trimmed proof, whereas for the very best clauses the LBD score gives stronger correlation. This gives more rigorous support for the currently popular heuristic of prioritizing clauses with very good LBD scores but sorting the rest of the clauses with respect to activity when deciding which clauses to erase. We remark that for these conclusions, it is crucial to use the actual proof found by the solver rather than the one reconstructed from the DRAT proof log.
AB - We propose that CDCL SAT solver heuristics such as restarts and clause database management can be analysed by studying the resolution proofs produced by the solvers, and by trimming these proofs to extract the clauses actually used to reach the final conclusion. We find that for non-adaptive Luby restarts higher frequency makes both untrimmed and trimmed proofs smaller, while adaptive restarts based on literal block distance (LBD) decrease proof size further mainly for untrimmed proofs. This seems to indicate that restarts improve the reasoning power of solvers, but that making restarts adaptive mainly helps to avoid useless work that is not needed to reach the end result. For clause database management we find that switching off clause erasures often, though not always, leads to smaller untrimmed proofs, but has no significant effect on trimmed proofs. With respect to quality measures for learned clauses, activity in conflict analysis is a fairly good predictor in general for a clause ending up also in the trimmed proof, whereas for the very best clauses the LBD score gives stronger correlation. This gives more rigorous support for the currently popular heuristic of prioritizing clauses with very good LBD scores but sorting the rest of the clauses with respect to activity when deciding which clauses to erase. We remark that for these conclusions, it is crucial to use the actual proof found by the solver rather than the one reconstructed from the DRAT proof log.
U2 - 10.1007/978-3-030-58475-7_25
DO - 10.1007/978-3-030-58475-7_25
M3 - Paper in conference proceeding
AN - SCOPUS:85091284248
SN - 9783030584740
T3 - Lecture Notes in Computer Science
SP - 427
EP - 444
BT - Principles and Practice of Constraint Programming - 26th International Conference, CP 2020, Proceedings
A2 - Simonis, Helmut
PB - Springer
T2 - 26th International Conference on Principles and Practice of Constraint Programming, CP 2020
Y2 - 7 September 2020 through 11 September 2020
ER -