Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 138 tok/s
Gemini 2.5 Pro 49 tok/s Pro
GPT-5 Medium 24 tok/s Pro
GPT-5 High 31 tok/s Pro
GPT-4o 92 tok/s Pro
Kimi K2 189 tok/s Pro
GPT OSS 120B 450 tok/s Pro
Claude Sonnet 4.5 34 tok/s Pro
2000 character limit reached

An Experimental Study of Permanently Stored Learned Clauses (2110.14187v1)

Published 27 Oct 2021 in cs.AI and cs.LO

Abstract: Modern CDCL SAT solvers learn clauses rapidly, and an important heuristic is the clause deletion scheme. Most current solvers have two (or more) stores of clauses. One has ``valuable'' clauses which are never deleted. Most learned clauses are added to the other, with an aggressive deletion strategy to restrict its size. Recent solvers in the MapleSAT family, have comparatively complex deletion scheme, and perform well. Many solvers store only binary clauses permanently, but MapleLCMDistChronoBT stores clauses with small LBD permanently. We report an experimental study of the permanent clause store in MapleLCMDistChronoBT. We observe that this store can get quite large, but several methods for limiting its size reduced performance. We also show that alternate size and LBD based criteria improve performance, while still having large permanent stores. In particular, saving clauses up to size 8, and adding small numbers of high-centrality clauses, both improved performance, with the best improvement using both methods.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.