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Proposition 16.2 -- the LLL-reversal chain is far from mixed at (1−ε)n2log⁡n(1-\varepsilon)\frac n2\log n(1−ε)2n​logn

Proved
MarkovMixing.l_reversal_lower

by Shuze Chen · Aug 21, 2026 · Mathlib 0df444a (Lean v4.33.1)

markov-chainsmixing-timesprobability

The LLL-reversal chain shuffles a circular arrangement of nnn cards (an arrangement indexed by Zn\mathbb Z_nZn​): at each step it picks a starting position i∈Zni\in\mathbb Z_ni∈Zn​ and a length k∈{0,…,L−1}k\in\{0,\dots,L-1\}k∈{0,…,L−1} uniformly at random and reverses the circular arc of cards from position iii to position i+ki+ki+k, leaving the rest fixed. Its stationary distribution is uniform. Write Pt(x,⋅)P^t(x,\cdot)Pt(x,⋅) for the law after ttt moves from the arrangement xxx, ∥μ−ν∥TV=max⁡A∣μ(A)−ν(A)∣\|\mu-\nu\|_{TV}=\max_A|\mu(A)-\nu(A)|∥μ−ν∥TV​=maxA​∣μ(A)−ν(A)∣ for the total variation distance, and dn(t)=max⁡x∥Pt(x,⋅)−unif∥TVd_n(t)=\max_x\|P^t(x,\cdot)-\mathrm{unif}\|_{TV}dn​(t)=maxx​∥Pt(x,⋅)−unif∥TV​ for the chain on nnn cards.

The theorem (Proposition 16.2 of Levin–Peres–Wilmer) asserts: for any family of maximal reversal lengths L(n)L(n)L(n) with 1≤L(n)<n/21\le L(n)<n/21≤L(n)<n/2 and any fixed 0<ε<10<\varepsilon<10<ε<1, evaluating each chain at time tn=⌊(1−ε) n2log⁡n⌋t_n=\bigl\lfloor(1-\varepsilon)\,\tfrac n2\log n\bigr\rfloortn​=⌊(1−ε)2n​logn⌋ gives

dn(tn)  ⟶  1(n→∞).d_n(t_n)\;\longrightarrow\;1\qquad(n\to\infty).dn​(tn​)⟶1(n→∞).

Before time n2log⁡n\tfrac n2\log n2n​logn the chain is asymptotically as far from uniform as possible, whatever LLL is. The witness is again coupon-collector-flavoured: cards whose neighbourhood no reversal has yet touched retain their original neighbours in the circular order. (In the formal statement the deck sizes are enumerated as n=m+2n=m+2n=m+2, m→∞m\to\inftym→∞, so that n≥2n\ge2n≥2 automatically.)

Preamble
import Definitions.Def_mm_shuffle
import Mathlib.Analysis.SpecialFunctions.Log.Basic
Formal statement
namespace MarkovMixing

/-- **Proposition 16.2** (LPW): for the family of `L`-reversal chains with
`1 ≤ L(n) < n/2` and any fixed `0 < ε < 1`, at time
`t(n) = (1−ε)(n/2) log n` the distance to stationarity tends to `1`:
the chain is far from mixed.  (States are indexed by `m` with `n = m + 2`.) -/
theorem l_reversal_lower (L : ℕ → ℕ)
    (hL : ∀ n : ℕ, 3 ≤ n → 1 ≤ L n ∧ 2 * L n < n)
    (ε : ℝ) (hε : 0 < ε) (hε1 : ε < 1) :
    Filter.Tendsto
      (fun m : ℕ =>
        distStationary (groupWalk (lReversalDist (m + 2) (L (m + 2))))
          (uniformDist (Equiv.Perm (ZMod (m + 2))))
          ⌊(1 - ε) * ((m : ℝ) + 2) / 2 * Real.log ((m : ℝ) + 2)⌋₊)
      Filter.atTop (nhds 1) := by
  sorry

end MarkovMixing
Source
D. A. Levin, Y. Peres, E. L. Wilmer, Markov Chains and Mixing Times, AMS 2009, https://documents.epfl.ch/groups/i/ip/ipg/www/2013-2014/Random_Walks/markovmixing.pdf, Section 16.2.1, Proposition 16.2, p. 222

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