Local consistency and SAT−solvers
In this paper we show that the power of using k-consistency techniques in a constraint problem is precisely captured by using a particular inference rule, which we call positive-hyper-resolution, on the direct Boolean encoding of the CSP instance. We also show that current clause-learning SAT-solver...
| Main Authors: | , |
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| Format: | Conference item |
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Springer
2010
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| _version_ | 1797053854914707456 |
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| author | Jeavons, P Petke, J |
| author_facet | Jeavons, P Petke, J |
| author_sort | Jeavons, P |
| collection | OXFORD |
| description | In this paper we show that the power of using k-consistency techniques in a constraint problem is precisely captured by using a particular inference rule, which we call positive-hyper-resolution, on the direct Boolean encoding of the CSP instance. We also show that current clause-learning SAT-solvers will deduce any positive-hyper-resolvent of a fixed size from a given set of clauses in polynomial expected time. We combine these two results to show that, without being explicitly designed to do so, current clause-learning SAT-solvers efficiently simulate k-consistency techniques, for all values of k. We then give some experimental results to show that this feature allows clause-learning SAT-solvers to efficiently solve certain families of CSP instances which are challenging for conventional CP solvers. |
| first_indexed | 2024-03-06T18:49:23Z |
| format | Conference item |
| id | oxford-uuid:0faaa4c3-18b6-4f25-adc3-081cc21e707e |
| institution | University of Oxford |
| last_indexed | 2024-03-06T18:49:23Z |
| publishDate | 2010 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:0faaa4c3-18b6-4f25-adc3-081cc21e707e2022-03-26T09:52:25ZLocal consistency and SAT−solversConference itemhttp://purl.org/coar/resource_type/c_5794uuid:0faaa4c3-18b6-4f25-adc3-081cc21e707eDepartment of Computer ScienceSpringer2010Jeavons, PPetke, JIn this paper we show that the power of using k-consistency techniques in a constraint problem is precisely captured by using a particular inference rule, which we call positive-hyper-resolution, on the direct Boolean encoding of the CSP instance. We also show that current clause-learning SAT-solvers will deduce any positive-hyper-resolvent of a fixed size from a given set of clauses in polynomial expected time. We combine these two results to show that, without being explicitly designed to do so, current clause-learning SAT-solvers efficiently simulate k-consistency techniques, for all values of k. We then give some experimental results to show that this feature allows clause-learning SAT-solvers to efficiently solve certain families of CSP instances which are challenging for conventional CP solvers. |
| spellingShingle | Jeavons, P Petke, J Local consistency and SAT−solvers |
| title | Local consistency and SAT−solvers |
| title_full | Local consistency and SAT−solvers |
| title_fullStr | Local consistency and SAT−solvers |
| title_full_unstemmed | Local consistency and SAT−solvers |
| title_short | Local consistency and SAT−solvers |
| title_sort | local consistency and sat solvers |
| work_keys_str_mv | AT jeavonsp localconsistencyandsatsolvers AT petkej localconsistencyandsatsolvers |