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Adaptive Application of SAT Solving Techniques

Adaptive Application of SAT Solving Techniques. Ohad Shacham and Karen Yorav Presented by Sharon Barner. Agenda. DPLL SAT Solvers – Terms Motivation Performance Metrics Adaptive Solving Experimentation Conclusion. DPLL SAT Solvers. Input: Boolean Formula, in CNF

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Adaptive Application of SAT Solving Techniques

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  1. Adaptive Application of SAT Solving Techniques Ohad Shacham and Karen Yorav Presented by Sharon Barner

  2. Agenda • DPLL SAT Solvers – Terms • Motivation • Performance Metrics • Adaptive Solving • Experimentation • Conclusion

  3. DPLL SAT Solvers • Input: Boolean Formula, in CNF • Output: Satisfying assignment / Unsatisfiable • Iterative exhaustive search • Decisions • Assign a value to a single variable – Decision Heuristic • increment decision level • BCP • propagate effect of assignment • Learning • when a conflict occurs, add a “conflict clause” to the database so that the same combination of assignments will not happen again • backtrack appropriately, decrement decision level

  4. Motivation • SAT solving is based on heuristics and strategies • There is no winning strategy • Best choice cannot be determined beforehand Previous Solutions • Learning from a training set • does not work well for BMC – no representative set • Choosing on-the-fly according to biased random function • applicable only to decision heuristic • does not stabilize on best option Lagoudakis & Littman 2001, Nudelman et al 2004 Herbstritt & Becker 2003

  5. Solution – Adaptive Solving • Switching options when not progressing well • Track the progress of the search on-the-fly Performance Metric • Decision Heuristic • Clause Deletion • Conflict Clause Generation Algorithm • Clause Replication • ...

  6. Performance Metrics • Produces a numerical score • Calculated On-The-Fly • Every fixed number of decisions evaluate the metric • Cheap to evaluate • Calculating the space to be searched is not an option... [SATometer] • Must rely on readily available information • Corresponds (roughly) to the effectiveness of the search

  7. Metrics • Average Decision Level • Too high - implies the solver may be “stuck” on a small space with no solution • Acceptable level varies between solvers • Average Conflict Clause Size • Smaller clauses potentially reduce the space more significantly • Percentage of Binary Conflict Clauses • Binary clauses are beneficial because they cause implications with little overhead

  8. More Metrics • BCP Ratio • Average number of steps per clause v1 v1 0 • A high BCP Ratio means the solver makes less decisions per second

  9. More Metrics • Unary Clause Learning • Permanent values for variables • Each one reduces the search space by half And others ...

  10. Adaptive Solving • Evaluate the performance metric every fixed number of decisions • Given the metric score, decide whether to make a switch • Can have different switching conditions for different options • Adaptive Solving requires tuning ! • Choose the parameters wisely • Tune the metric to the chosen parameter • Tune the metric to the solver structure • Tune according to the chosen domain

  11. Insights • The Parameter to control • capable of high impact on performance – both ways • easy to switch • The Sample Size • Large enough to allow a change to take effect • Switching • Better to disable switches for a while after a switch is performed • Switching condition becomes stronger after each switch • Total limit on the number of switches

  12. Experimentation • IBM benchmarks • Time out set to 10000 seconds • No time outs to prevent the time out constant from influencing speedup results • Parameter controlled – the value given to a decision variable • first the decision variable is chosen, and only then the value • by default - value is according to the literal with the higher score • attempting to satisfy more clauses • -sign option switches the choice to the literal with the lower score • attempting to generate more conflicts • In general, the default is much better • in some cases “-sign” improves run times significantly

  13. Maximum Speedup is on larger example Detrimental effect more pronounced on smaller examples Global Runtime Native Global Runtime Adaptive Works better on UNSAT instances Experimentation

  14. Conclusion • Adaptive solving enables making use of ideas that don’t always work • Enabling an option on parts of the search space can give better results than enabling it or disabling it all of the time! • Even when the option is inherently bad for this example • Need more and better metrics • Combine metrics • Relate metrics to the parameter they control • Can also be applied in-between BMC instances • be careful – the “best configuration” for short instances is not the best for long ones(experimentation found no correlation)

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