Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
175 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Probabilistic estimates of the diameters of the Rubik's Cube groups (2404.07337v7)

Published 10 Apr 2024 in cs.DM, cs.DS, math.CO, and math.PR

Abstract: The diameter of the Cayley graph of the Rubik's Cube group is the fewest number of turns needed to solve the Cube from the hardest initial configuration. For the 2$\times$2$\times$2 Cube, the diameter is 11 in the half-turn metric, 14 in the quarter-turn metric, 19 in the semi-quarter-turn metric, and 10 in the bi-quarter-turn metric. For the 3$\times$3$\times$3 Cube, the diameter was determined by Rokicki et al. to be 20 in the half-turn metric and 26 in the quarter-turn metric. This study shows that a modified version of the coupon collector's problem in probability theory can predict the diameters correctly for both 2$\times$2$\times$2 and 3$\times$3$\times$3 Cubes insofar as the quarter-turn metric is adopted. In the half-turn metric, the diameters are overestimated by one and two, respectively, for the 2$\times$2$\times$2 and 3$\times$3$\times$3 Cubes, whereas for the 2$\times$2$\times$2 Cube in the semi-quarter-turn and bi-quarter-turn metrics, they are overestimated by two and underestimated by one, respectively. Invoking the same probabilistic logic, the diameters of the 4$\times$4$\times$4 and 5$\times$5$\times$5 Cubes are predicted to be 48 (41) and 68 (58) in the quarter-turn (half-turn) metric, whose precise determinations are far beyond reach of classical supercomputing. The probabilistically estimated diameter is shown to obey the approximate formula of $\ln N / \ln r + \ln N / r$, where $N$ is the number of configurations and $r$ is the branching ratio.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (30)
  1. D. R. Hofstadter, Sci. Am. 244, 20 (1981).
  2. D. Singmaster, Note on Rubik’s Magic Cube (Enslow Publishers, Hillside, NJ, 1981).
  3. D. Ferenc, “Ruwix: The Rubik’s Cube and Twisty Puzzle Wiki,” https://ruwix.com (2024).
  4. M. Thistlethwaite, ‘‘Thistlethwaite’s 52-move algorithm,” https://www.jaapsch.net/puzzles/thistle.htm (1981).
  5. H. Kociemba, “The Two-Phase-Algorithm,” http://kociemba.org/cube.htm (1992).
  6. D. Kunkle and G. Cooperman, “Twenty-six moves suffice for Rubik’s cube,” in Proceedings of the 2007 International Symposium on Symbolic and Algebraic Computation (2007) pp. 235–242.
  7. D. Kunkle and G. Cooperman, J. Symb. Comput. 44, 872 (2009).
  8. T. Rikicki, H. Kociemba, M. Davidson,  and J. Dethridge, “God’s Number is 20,” https://www.cube20.org (2014).
  9. R. E. Korf, Artif. Intell. 26, 35 (1985).
  10. H. Zassenhaus, Physica A 114, 629 (1982).
  11. A. Fiat, S. Moses, A. Shamir, I. Shimshoni,  and G. Tardos, “Planning and learning in permutation groups,” in 30th Annual Symposium on Foundations of Computer Science (1989) pp. 274–279.
  12. D. Ryabogin, Adv. Math. 231, 3429 (2012).
  13. C. L. Lee and M. C. Huang, Eur. Phys. J. B 64, 257 (2008).
  14. X. Chen and Z. J. Ding, Comput. Phys. Commun. 183, 1658 (2012).
  15. Y. R. Chen and C. L. Lee, Phys. Rev. E 89, 012815 (2014).
  16. A. V. Diaconu and K. Loukhaoukha, Math. Probl. Eng. 2013, 848392 (2013).
  17. V. M. Ionesco and A. V. Diaconu, “Rubik’s cube principle based image encryption algorithm implementation on mobile devices,” in Proceedings of the 2015 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (2015) pp. P31–P34.
  18. P. Lichodzijewski and M. Heywood, “The Rubik Cube and GP temporal sequence learning: An initial study,” in Genetic Programming Theory and Practice VIII (2011) pp. 35–54.
  19. R. J. Smith, S. Kelly,  and M. I. Heywood, “Discovering Rubik’s Cube subgroups using coevolutionary GP: A five twist experiment,” in Proceedings of the 2016 Genetic and Evolutionary Computation Conference (2016) pp. 789–796.
  20. C. G. Johnson, “Solving the Rubik’s Cube with learned guidance functions,” in 2018 IEEE Symposium Series on Computational Intelligence (2018) pp. 2082–2089.
  21. C. G. Johnson, Expert Syst. 38, e12665 (2021).
  22. R. E. Korf, “Finding optimal solutions to Rubik’s Cube using pattern databases,” in Proceedings of the 14th AAAI Conference on Artificial Intelligence (1997) pp. 700–705.
  23. J. C. Culberson and J. Schaeffer, Comput. Intell. 14, 318 (1998).
  24. R. E. Korf and A. Felner, Artif. Intell. 134, 9 (2002).
  25. P. Jackson, Introduction to Expert Systems, 3rd ed. (Addison-Wesley, 1998).
  26. W.-M. Shen, Artif. Intell. 41, 257 (1990).
  27. E. J. Gumbel, Ann. Math. Stat. 12, 163 (1941).
  28. B. Dawkins, Am. Stat. 45, 76 (1991).
  29. D. Singmaster, ‘‘Cubic Circular, Issue 3/4,” https://www.jaapsch.net/puzzles/cubic3.htm#p14 (1982).
  30. S. Hirata, “rubik,” https://github.com/sohirata/rubik (2014).
Citations (2)

Summary

We haven't generated a summary for this paper yet.