Abstract
Phase transitions in constraint satisfaction problems (CSP's) are the subject of intense study. We identify a control parameter for random binary CSP's. There is a rapid transition in the probability of a CSP having a solution at a critical value of this parameter. This parameter allows different phase transition behaviour to be compared in an uniform manner, for example CSP's generated under different regimes. We then show that within classes, the scaling of behaviour can be modelled by a technique called “finite size scaling”. This applies not only to probability of solubility, as has been observed before in other NP-problems, but also to search cost. Furthermore, the technique applies with equal validity to several different methods of varying problem size. As well as contributing to the understanding of phase transitions, we contribute by allowing much finer grained comparison of algorithms, and for accurate empirical extrapolations of behaviour.
The fourth author is supported by an HCM Postdoctoral Fellowship. We thank the Department of Computer Science at the University of Strathclyde for CPU cycles, and Judith Underwood for her help.
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References
M.N. Barber. Finite-size scaling. In Phase Transitions and Critical Phenomena, Volume 8, pages 145–266. Academic Press, 1983.
P. Cheeseman, B. Kanefsky, and W.M. Taylor. Where the really hard problems are. In Proceedings of the 12th IJCAI, pages 331–337. International Joint Conference on Artificial Intelligence, 1991.
R. Dechter, Constraint Networks, in Encyclopedia of Artificial Intelligence, Wiley, New York, 2nd ed., 276–286, 1992.
R. Dechter and I. Meiri, Experimental evaluation of preprocessing algorithms for constraint satisfaction problems, Artif. Intell. 68(2) (1994) 211–242.
D. Frost and R. Dechter, In search of the best search: an empirical evaluation, Proceedings AAAI-94, Seattle, WA (1994) 301–306.
J. Gaschnig, A general backtracking algorithm that eliminates most redundant tests, Proceedings IJCAI-77, Cambridge, MA (1977) 457.
J. Gaschnig, Performance measurement and analysis of certain search algorithms, Tech. Rept. CMU-CS-79-124, Carnegie-Mellon University, Pittsburgh, PA (1979).
I. P. Gent and T. Walsh. The SAT phase transition. In Proceedings of ECAI-94, pages 105–109, 1994.
I.P. Gent and T. Walsh. The satisfiability constraint gap. To appear in Artificial Intelligence.
I.P. Gent and T. Walsh. The TSP phase transition. Research report 95-178, Department of Computer Science, University of Strathclyde, 1995. Presented at First Workshop on AI and OR, Timberline, Oregon.
S.W. Golomb and L.D. Baumert, Backtrack programming. JACM 12 (1965) 516–524.
R.M. Haralick and G.L. Elliott, Increasing Tree Search Efficiency for Constraint Satisfaction Problems, Artif. Intell. 14 (1980) 263–313.
T. Hogg and C. Williams. The hardest constraint problems: A double phase transition. Artificial Intelligence, 69:359–377, 1994.
J.N. Hooker, Needed: An empirical science of algorithms, Operations Research 42 (2) (1994) 201–212.
S. Kirkpatrick, G. Györgyi, N. Tishby, and L. Troyansky. The statistical mechanics of k-satisfaction. In Advances in Neural Information Processing Systems 6, pages 439–446. Morgan Kaufmann, 1994.
S. Kirkpatrick and B. Selman. Critical behavior in the satisfiability of random boolean expressions. Science, 264:1297–1301, May 27 1994.
V. Kumar, Algorithms for constraint satisfaction problems: a survey, AI magazine 13(1) (1992) 32–44.
E. MacIntyre, Really hard problems, Final Year Report for the B.Sc. degree, Department of Computer Science, University of Strathclyde, Scotland, 1994.
D. Mitchell, B. Selman, and Hector Levesque. Hard and easy distributions of SAT problems. In Proceedings, 10th National Conference on Artificial Intelligence. AAAI Press/The MIT Press, pages 459–465, 1992.
P. Prosser. Hybrid algorithms for the constraint satisfaction problem. Computational Intelligence, 9:268–299, 1993.
P. Prosser. Binary constraint satisfaction problems: Some are harder than others. In Proceedings of ECAI-94, pages 95–99, 1994.
P. Prosser. An empirical study of phase transitions in binary constraint satisfaction problems. To appear in Artificial Intelligence.
P.W. Purdom, Search rearrangement backtracking and polynomial average time, Artif. Intell. 21 (1983) 117–133.
D. Sabin and E.C. Freuder, Contradicting conventional wisdom in constraint satisfaction, Proceedings ECAI-94, Amsterdam, The Netherlands (1994) 125–129.
B. Selman and S. Kirkpatrick. Critical behaviour in the computational cost of satisfiability testing. To appear in Artificial Intelligence.
B.M Smith. Phase transition and the mushy region in constraint satisfaction problems. In Proceedings of ECAI-94, pages 100–104, 1994.
B. M. Smith and M.E. Dyer. Locating the phase transition in binary constraint satisfaction problems. To appear in Artificial Intelligence.
E.P.K. Tsang, Foundations of Constraint Satisfaction, Academic Press, 1993.
E.P.K. Tsang, J. Borrett, and A.C.M. Kwan, An attempt to map the performance of a range of algorithm and heuristic combinations, In Proceedings AISB-95, pages 203–216, Ed. J. Hallam, IOS Press, Amsterdam, 1995.
C.P. Williams and T. Hogg. Exploiting the deep structure of constraint problems. Artificial Intelligence, 70:73–117, 1994.
K.G. Wilson. Problems in physics with many scales of length. Scientific American, 241:140–157, 1979.
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Gent, I.P., MacIntyre, E., Prosser, P., Walsh, T. (1995). Scaling effects in the CSP phase transition. In: Montanari, U., Rossi, F. (eds) Principles and Practice of Constraint Programming — CP '95. CP 1995. Lecture Notes in Computer Science, vol 976. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60299-2_5
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DOI: https://doi.org/10.1007/3-540-60299-2_5
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