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Theory of Computing Systems

, Volume 63, Issue 5, pp 1068–1088 | Cite as

Mixing of Permutations by Biased Transpositions

  • Shahrzad HaddadanEmail author
  • Peter Winkler
Article
  • 19 Downloads
Part of the following topical collections:
  1. Special Issue on Theoretical Aspects of Computer Science (STACS 2017)

Abstract

We prove rapid mixing for certain Markov chains on the set Sn of permutations on 1,2,…,n in which adjacent transpositions are made with probabilities that depend on the items being transposed. Typically, when in state σ, a position i < n is chosen uniformly at random, and σ(i) and σ(i+ 1) are swapped with probability depending on σ(i) and σ(i+ 1). The stationary distributions of such chains appear in various fields of theoretical computer science (Wilson, Ann. Appl. Probab. 1:274–325, 2004, Diaconis and Shahshahani, Probab. Theory Relat. Fields 57:159–179, 1981, Chierichetti, et al. 2014), and rapid mixing established in the uniform case (Wilson, Ann. Appl. Probab. 1:274–325, 2004). Recently, there has been progress in cases with biased stationary distributions (Benjamini, et al. Trans. Am. Math. Soc. 357, 3013–3029 2005, Bhakta, et al. 2014), but there are wide classes of such chains whose mixing time is unknown. One case of particular interest is what we call the “gladiator chain,” in which each number g is assigned a “strength” sg and when g and g are adjacent and chosen for possible swapping, g comes out on top with probability \(s_{g}/(s_{g} + s_{g^{\prime }})\). We obtain a polynomial-time upper bound on mixing time when the gladiators fall into only three strength classes. A preliminary version of this paper appeared as “Mixing of Permutations by Biased Transposition” in STACS 2017 (Haddadan and Winkler, 2017).

Keywords

Markov chains Permutations Self organizing lists Mixing time 

Notes

Acknowledgements

We would like to thank Dana Randall for a very helpful conversation about the gladiator problem and Fill’s conjecture, and Sergi Elizalde for his help and knowledge concerning generating functions.

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Universita degli Studi di Roma La Sapienza Viale Regina Elena, 295bRomeItaly
  2. 2.Dartmouth CollegeHanoverUSA

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