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Exponential Separations in a Hierarchy of Clause Learning Proof Systems

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Theory and Applications of Satisfiability Testing – SAT 2013 (SAT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7962))

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Abstract

Resolution trees with lemmas (RTL) are a resolution-based propositional proof system that is related to the DPLL algorithm with clause learning. Its fragments \(\ensuremath{\ensuremath{\mathrm{RTL}} ({k})}\) are related to clause learning algorithms where the width of learned clauses is bounded by k.

For every k up to O(logn), an exponential separation between the proof systems \(\ensuremath{\ensuremath{\mathrm{RTL}} ({k})}\) and \(\ensuremath{\ensuremath{\mathrm{RTL}} ({k+1})}\) is shown.

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References

  1. Bayardo Jr., R.J., Schrag, R.C.: Using CSP look-back techniques to solve real-world SAT instances. In: Proc. 14th Natl. Conference on Artificial Intelligence, pp. 203–208 (1997)

    Google Scholar 

  2. Ben-Sasson, E.: Size-space tradeoffs for resolution. SIAM Journal on Computing 38(6), 2511–2525 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  3. Ben-Sasson, E., Impagliazzo, R., Wigderson, A.: Near-optimal separation of general and tree-like resolution. Combinatorica 24(4), 585–604 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  4. Ben-Sasson, E., Johannsen, J.: Lower bounds for width-restricted clause learning on small width formulas. In: Strichman, O., Szeider, S. (eds.) SAT 2010. LNCS, vol. 6175, pp. 16–29. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  5. Ben-Sasson, E., Nordström, J.: Understanding space in proof complexity: Separations and trade-offs via substitutions. In: Chazelle, B. (ed.) Innovations in Computer Science, ICS 2011, pp. 401–416. Tsinghua University Press (2011)

    Google Scholar 

  6. Buss, S.R., Hoffmann, J., Johannsen, J.: Resolution trees with lemmas: Resolution refinements that characterize DLL algorithms with clause learning. Logical Methods in Computer Science 4(4) (2008)

    Google Scholar 

  7. Celoni, J., Paul, W., Tarjan, R.: Space bounds for a game on graphs. Mathematical Systems Theory 10, 239–251 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  8. Davis, M., Logemann, G., Loveland, D.W.: A machine program for theorem-proving. Communications of the ACM 5(7), 394–397 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  9. Davis, M., Putnam, H.: A computing procedure for quantification theory. Journal of the Association for Computing Machinery 7(3), 201–215 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  10. Gomes, C.P., Selman, B., Crato, N.: Heavy-tailed distributions in combinatorial search. In: Smolka, G. (ed.) CP 1997. LNCS, vol. 1330, pp. 121–135. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  11. Håstad, J.: The shrinkage exponent of De Morgan formulas is 2. SIAM Journal on Computing 27(1), 48–64 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hertel, P., Bacchus, F., Pitassi, T., van Gelder, A.: Clause learning can effectively p-simulate general propositional resolution. In: Fox, D., Gomes, C.P. (eds.) Proceedings of the 23rd AAAI Conference on Artificial Intelligence, AAAI 2008, pp. 283–290. AAAI Press (2008)

    Google Scholar 

  13. Johannsen, J.: An exponential lower bound for width-restricted clause learning. In: Kullmann, O. (ed.) SAT 2009. LNCS, vol. 5584, pp. 128–140. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  14. Marques-Silva, J.P., Sakallah, K.A.: GRASP - a new search algorithm for satisfiability. In: Proc. IEEE/ACM International Conference on Computer Aided Design, ICCAD, pp. 220–227 (1996)

    Google Scholar 

  15. Tseitin, G.S.: On the complexity of derivation in propositional calculus. Studies in Constructive Mathematics and Mathematical Logic, Part 2, 115–125 (1968)

    Google Scholar 

  16. Urquhart, A.: A near-optimal separation of regular and general resolution. SIAM Journal on Computing 40(1), 107–121 (2011)

    Article  MathSciNet  MATH  Google Scholar 

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Johannsen, J. (2013). Exponential Separations in a Hierarchy of Clause Learning Proof Systems. In: Järvisalo, M., Van Gelder, A. (eds) Theory and Applications of Satisfiability Testing – SAT 2013. SAT 2013. Lecture Notes in Computer Science, vol 7962. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39071-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-39071-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

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