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Efficient Clause Deletion Strategy for SAT Solver Optimization

Explore the benefits of clause deletion in SAT solvers, balancing deletion criteria for optimal performance, and the impact on search tree pruning and memory usage. Learn from GRASP algorithm insights and experimental results.

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Efficient Clause Deletion Strategy for SAT Solver Optimization

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  1. Clause Deletion Strategy in a Satisfiability Solver Presented by Colin Southwood For CMPS 217, Logic in Computer Science Tuesday, December 11th, 2007

  2. Conflict, or ‘Learnt’ Clauses • 1996 GRASP– “Generic seaRch Algorithm for the Satisfiability Problem” by Joao P. Marques Silva and Karem A. Sakallah • Introduced the idea of recording the causes of conflicts • Prunes the Search Tree

  3. Problems with accumulating Conflict Clauses • Takes up Space, makes using the lessons depend on the access times of main memory and possibly of disk • Requires BCP take more time, when it already takes up a lot of time ( BCP already takes %90 of time for solving SAT problems )

  4. Experimental Results

  5. No Clause Deletion

  6. Calls to BCP procedure; more calls, but less time

  7. Calls to BCP procedure; fewer calls, but more time

  8. Activity an additional constraint; fewer are deleted

  9. learnts more easily sat. criteria Of deletion; Num. learnts rarely exceeds limit

  10. How to deal with knowing too much Delete no clauses? Delete “weaker” clauses? Delete “low activity” clauses? No. Yes. Yes. In the right balance. It is a balancing act. Don’t delete too many, don’t delete too few!

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