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research-article

Space Complexity in Polynomial Calculus

Published: 01 January 2015 Publication History

Abstract

During the last 10 to 15 years, an active line of research in proof complexity has been to study space complexity and time-space trade-offs for proofs. Besides being a natural complexity measure of intrinsic interest, space is also an important concern in SAT solving, and so research has mostly focused on weak systems that are used by SAT solvers. There has been a relatively long sequence of papers on space in resolution, which is now reasonably well-understood from this point of view. For other proof systems of interest, however, such as polynomial calculus or cutting planes, progress has been more limited. Essentially nothing has been known about space complexity in cutting planes, and for polynomial calculus the only lower bound has been for conjunctive normal form (CNF) formulas of unbounded width in [Alekhnovich et al., SIAM J. Comput., 31 (2002), pp. 1184--1211], where the space lower bound is smaller than the initial width of the clauses in the formulas. Thus, in particular, it has been consistent with current knowledge that polynomial calculus could be able to refute any $k$-CNF formula in constant space. In this paper, we prove several new results on space in polynomial calculus (PC) and in the extended proof system polynomial calculus resolution (PCR) studied by Alekhnovich et al.: (1) We prove an $\omega(n)$ space lower bound in PC for the canonical 3-CNF version of the pigeonhole principle formulas $PHP_{m}^{n}$ with $m$ pigeons and $n$ holes, and show that this is tight. (2) For PCR, we prove an $\omega(n)$ space lower bound for a bitwise encoding of the functional pigeonhole principle. These formulas have width O(log n), and hence this is an exponential improvement over Alekhnovich et al. measured in the width of the formulas. (3) We then present another encoding of the pigeonhole principle that has constant width, and prove an $\omega(n)$ space lower bound in PCR for these formulas as well. (4) Finally, we prove that any $k$-CNF formula can be refuted in PC in simultaneous exponential size and linear space (which holds for resolution and thus for PCR, but was not obviously the case for PC). We also characterize a natural class of CNF formulas for which the space complexity in resolution and PCR does not change when the formula is transformed into 3-CNF in the canonical way, something that we believe can be useful when proving PCR space lower bounds for other well-studied formula families in proof complexity.

References

[1]
M. Alekhnovich, E. Ben-Sasson, A. A. Razborov, and A. Wigderson, Space complexity in propositional calculus, SIAM J. Comput., 31 (2002), pp. 1184--1211.
[2]
M. Alekhnovich and A. A. Razborov, Lower bounds for polynomial calculus: Non-binomial case, Proc. Steklov Inst. Math., 242 (2003), pp. 18--35; also available online from http://people.cs.uchicago.edu/ razborov/files/misha.pdf.
[3]
A. Atserias and V. Dalmau, A combinatorial characterization of resolution width, J. Comput. System Sci., 74 (2008), pp. 323--334.
[4]
A. Atserias, J. K. Fichte, and M. Thurley, Clause-learning algorithms with many restarts and bounded-width resolution, J. Artificial Intelligence Res., 40 (2011), pp. 353--373.
[5]
R. J. Bayardo, Jr., and R. Schrag, Using CSP look-back techniques to solve real-world SAT instances, in Proceedings of the 14th National Conference on Artificial Intelligence (AAAI '97), 1997, pp. 203--208.
[6]
P. Beame, Proof complexity, in Computational Complexity Theory, S. Rudich and A. Wigderson, eds., IAS/Park City Math. Ser. 10, AMS, Providence, RI, 2004, pp. 199--246.
[7]
P. Beame, C. Beck, and R. Impagliazzo, Time-space tradeoffs in resolution: Superpolynomial lower bounds for superlinear space, in Proceedings of the 44th Annual ACM Symposium on Theory of Computing (STOC '12), 2012, pp. 213--232.
[8]
P. Beame, R. Karp, T. Pitassi, and M. Saks, The efficiency of resolution and Davis-Putnam procedures, SIAM J. Comput., 31 (2002), pp. 1048--1075.
[9]
C. Beck, J. Nordström, and B. Tang, Some trade-off results for polynomial calculus, in Proceedings of the 45th Annual ACM Symposium on Theory of Computing (STOC '13), 2013, pp. 813--822.
[10]
E. Ben-Sasson, Size space tradeoffs for resolution, SIAM J. Comput., 38 (2009), pp. 2511--2525.
[11]
E. Ben-Sasson and N. Galesi, Space complexity of random formulae in resolution, Random Structures Algorithms, 23 (2003), pp. 92--109.
[12]
E. Ben-Sasson and R. Impagliazzo, Random CNF's are hard for the polynomial calculus, Comput. Complexity, 19 (2010), pp. 501--519.
[13]
E. Ben-Sasson and J. Johannsen, Lower bounds for width-restricted clause learning on small width formulas, in Proceedings of the 13th International Conference on Theory and Applications of Satisfiability Testing (SAT '10), Lecture Notes in Comput. Sci. 6175, Springer, New York, 2010, pp. 16--29.
[14]
E. Ben-Sasson and J. Nordström, Short proofs may be spacious: An optimal separation of space and length in resolution, in Proceedings of the 49th Annual IEEE Symposium on Foundations of Computer Science (FOCS '08), 2008, pp. 709--718.
[15]
E. Ben-Sasson and J. Nordström, Understanding space in proof complexity: Separations and trade-offs via substitutions, in Proceedings of the 2nd Symposium on Innovations in Computer Science (ICS '11), 2011, pp. 401--416.
[16]
E. Ben-Sasson and A. Wigderson, Short proofs are narrow---resolution made simple, J. ACM, 48 (2001), pp. 149--169.
[17]
A. Biere, M. J. H. Heule, H. van Maaren, and T. Walsh, Eds., Handbook of Satisfiability, Frontiers Artificial Intelligence Appl. 185, IOS Press, Amsterdam, 2009.
[18]
A. Blake, Canonical Expressions in Boolean Algebra, Ph.D. thesis, University of Chicago, 1937.
[19]
I. Bonacina and N. Galesi, Pseudo-partitions, transversality and locality: A combinatorial characterization for the space measure in algebraic proof systems, in Proceedings of the 4th Conference on Innovations in Theoretical Computer Science (ITCS '13), 2013, pp. 455--472.
[20]
I. Bonacina, N. Galesi, T. Huynh, and P. Wollan, Space proof complexity for random $3$-CNFs via a $(2-\epsilon)$-Hall's theorem, Technical report TR14-146, Electronic Colloquium on Computational Complexity (ECCC), 2014.
[21]
I. Bonacina, N. Galesi, and N. Thapen, Total space in resolution, in Proceedings of the 55th Annual IEEE Symposium on Foundations of Computer Science (FOCS '14), 2014, pp. 641--650.
[22]
M. Bonet, T. Pitassi, and R. Raz, Lower bounds for cutting planes proofs with small coefficients, in Proceedings of the 27th Annual ACM Symposium on Theory of Computing (STOC '95), 1995, pp. 575--584.
[23]
M. Brickenstein and A. Dreyer, PolyBoRi: A framework for Gröbner-basis computations with Boolean polynomials, J. Symbolic Comput., 44 (2009), pp. 1326--1345.
[24]
M. Brickenstein, A. Dreyer, G.-M. Greuel, M. Wedler, and O. Wienand, New developments in the theory of Gröbner bases and applications to formal verification, J. Pure Appl. Algebra, 213 (2009), pp. 1612--1635.
[25]
S. R. Buss, D. Grigoriev, R. Impagliazzo, and T. Pitassi, Linear gaps between degrees for the polynomial calculus modulo distinct primes, J. Comput. System Sci., 62 (2001), pp. 267--289.
[26]
S. R. Buss, J. Hoffmann, and J. Johannsen, Resolution trees with lemmas: Resolution refinements that characterize DLL-algorithms with clause learning, Log. Methods Comput. Sci., 4 (2008).
[27]
S. R. Buss and T. Pitassi, Resolution and the weak pigeonhole principle, in Proceedings of the 11th International Workshop on Computer Science Logic (CSL '97), Lecture Notes in Comput. Sci. 1414, Springer, New York, 1998, pp. 149--156.
[28]
V. Chvátal and E. Szemerédi, Many hard examples for resolution, J. ACM, 35 (1988), pp. 759--768.
[29]
M. Clegg, J. Edmonds, and R. Impagliazzo, Using the Groebner basis algorithm to find proofs of unsatisfiability, in Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC '96), 1996, pp. 174--183.
[30]
S. A. Cook and R. Reckhow, The relative efficiency of propositional proof systems, J. Symbolic Logic, 44 (1979), pp. 36--50.
[31]
W. Cook, C. R. Coullard, and G. Turán, On the complexity of cutting-plane proofs, Discrete Appl. Math., 18 (1987), pp. 25--38.
[32]
M. Davis, G. Logemann, and D. Loveland, A machine program for theorem proving, Commun. ACM, 5 (1962), pp. 394--397.
[33]
M. Davis and H. Putnam, A computing procedure for quantification theory, J. ACM, 7 (1960), pp. 201--215.
[34]
J. L. Esteban and J. Torán, Space bounds for resolution, Inform. and Comput., 171 (2001), pp. 84--97.
[35]
Y. Filmus, M. Lauria, M. Mikša, J. Nordström, and M. Vinyals, Towards an understanding of polynomial calculus: New separations and lower bounds (extended abstract), in Proceedings of the 40th International Colloquium on Automata, Languages and Programming (ICALP '13), Lecture Notes in Comput. Sci. 7965, Springer, New York, 2013, pp. 437--448.
[36]
Y. Filmus, M. Lauria, J. Nordström, N. Thapen, and N. Ron-Zewi, Space complexity in polynomial calculus, in Proceedings of the 27th Annual IEEE Conference on Computational Complexity (CCC '12), 2012, pp. 334--344.
[37]
N. Galesi, P. Pudlák, and N. Thapen, The space complexity of cutting planes refutations, in Proceedings of the 30th Annual Computational Complexity Conference (CCC '15), 2015.
[38]
M. Göös and T. Pitassi, Communication lower bounds via critical block sensitivity, in Proceedings of the 46th Annual ACM Symposium on Theory of Computing (STOC '14), 2014, pp. 847--856.
[39]
A. Haken, The intractability of resolution, Theoret. Comput. Sci., 39 (1985), pp. 297--308.
[40]
T. Huynh and J. Nordström, On the virtue of succinct proofs: Amplifying communication complexity hardness to time-space trade-offs in proof complexity, in Proceedings of the 44th Annual ACM Symposium on Theory of Computing (STOC '12), 2012, pp. 233--248.
[41]
R. Impagliazzo, P. Pudlák, and J. Sgall, Lower bounds for the polynomial calculus and the Gröbner basis algorithm, Comput. Complexity, 8 (1999), pp. 127--144.
[42]
M. Järvisalo, A. Matsliah, J. Nordström, and S. Živný, Relating proof complexity measures and practical hardness of SAT, in Proceedings of the 18th International Conference on Principles and Practice of Constraint Programming (CP '12), Lecture Notes in Comput. Sci. 7514, Springer, New York, 2012, pp. 316--331.
[43]
J. P. Marques-Silva and K. A. Sakallah, GRASP: A search algorithm for propositional satisfiability, IEEE Trans. Comput., 48 (1999), pp. 506--521.
[44]
M. Mikša and J. Nordström, A generalized method for proving polynomial calculus degree lower bounds, in Proceedings of the 30th Annual Computational Complexity Conference (CCC '15), 2015.
[45]
M. W. Moskewicz, C. F. Madigan, Y. Zhao, L. Zhang, and S. Malik, Chaff: Engineering an efficient SAT solver, in Proceedings of the 38th Design Automation Conference (DAC '01), 2001, pp. 530--535.
[46]
J. Nordström, Narrow proofs may be spacious: Separating space and width in resolution, SIAM J. Comput., 39 (2009), pp. 59--121.
[47]
J. Nordström, A simplified way of proving trade-off results for resolution, Inform. Process. Lett., 109 (2009), pp. 1030--1035.
[48]
J. Nordström, Pebble games, proof complexity and time-space trade-offs, Log. Methods Comput. Sci., 9 (2013), pp. 15:1--15:63.
[49]
J. Nordström and J. H\aastad, Towards an optimal separation of space and length in resolution, Theory Comput., 9 (2013), pp. 471--557.
[50]
K. Pipatsrisawat and A. Darwiche, On the power of clause-learning SAT solvers as resolution engines, Artificial Intelligence, 175 (2011), pp. 512--525.
[51]
P. Pudlák, Lower bounds for resolution and cutting plane proofs and monotone computations, J. Symbolic Logic, 62 (1997), pp. 981--998.
[52]
A. A. Razborov, Lower bounds for the polynomial calculus, Comput. Complexity, 7 (1998), pp. 291--324.
[53]
J. A. Robinson, A machine-oriented logic based on the resolution principle, J. ACM, 12 (1965), pp. 23--41.
[54]
The international SAT Competitions, http://www.satcompetition.org.
[55]
N. Segerlind, The complexity of propositional proofs, Bull. Symbolic Logic, 13 (2007), pp. 482--537.
[56]
A. Urquhart, Hard examples for resolution, J. ACM, 34 (1987), pp. 209--219.

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Published In

cover image SIAM Journal on Computing
SIAM Journal on Computing  Volume 44, Issue 4
DOI:10.1137/smjcat.44.4
Issue’s Table of Contents

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Society for Industrial and Applied Mathematics

United States

Publication History

Published: 01 January 2015

Author Tags

  1. proof complexity
  2. space
  3. polynomial calculus
  4. polynomial calculus resolution
  5. PCR
  6. resolution
  7. lower bounds

Author Tags

  1. 03B05
  2. 03B35
  3. 03B70
  4. 03D15
  5. 03F20
  6. 68Q17
  7. 68T15

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