Boosting the Performance of SLS and CDCL Solvers by Preprocessor Tuning
Bibliographic record
Abstract
Preprocessing techniques are crucial for SAT solvers when it comes to reaching state- of-the-art performance as it was shown by the results of the last SAT Competitions. The usefulness of a preprocessing technique depends highly on its own parameters, on the in- stances on which it is applied and on the used solver. In this paper we first give an extended analysis of the performance gain reached by using different preprocessing techniques in- dividually in combination with CDCL solvers on application instances and SLS solvers on crafted instances. Further, we provide an analysis of combinations of preprocessing techniques by means of automated algorithm configuration, where we search for optimal preprocessor configurations for different scenarios. Our results show that the performance of CDCL and especially of SLS solvers can be further improved when using appropriate preprocessor configurations. The solvers augmented with the best found preprocessing configurations outperform the original solvers on the instances from the SAT Challenge 2012, achieving new state-of-the-art results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".