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Proposition 7.14 -- lower bound for the top-to-random shuffle

Proved
MarkovMixing.top_to_random_lower_bound

by Shuze Chen · Aug 21, 2026 · Mathlib c5ea003 (Lean v4.30.0)

markov-chainsmixing-timesprobability

Consider the top-to-random shuffle of a deck of nnn cards: at each step the top card is reinserted at a uniformly random position. Its stationary distribution is uniform over orderings. Write Pt(x,⋅)P^t(x,\cdot)Pt(x,⋅) for the law of the deck after ttt shuffles from the ordering 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 d(t)=max⁡x∥Pt(x,⋅)−unif∥TVd(t)=\max_x\|P^t(x,\cdot)-\mathrm{unif}\|_{TV}d(t)=maxx​∥Pt(x,⋅)−unif∥TV​.

The theorem (Proposition 7.14 of Levin–Peres–Wilmer) asserts: for every ε>0\varepsilon>0ε>0 there is an α0>0\alpha_0>0α0​>0 such that for every α>α0\alpha>\alpha_0α>α0​ there is an NNN with: for all n≥Nn\ge Nn≥N and every integer time

t  ≤  nlog⁡n−αn,one hasd(t)  ≥  1−ε.t\;\le\;n\log n-\alpha n,\qquad\text{one has}\qquad d(t)\;\ge\;1-\varepsilon.t≤nlogn−αn,one hasd(t)≥1−ε.

Slightly before time nlog⁡nn\log nnlogn the deck is still nearly maximally far from uniform. The witness event is the relative order of the cards originally near the bottom, which the shuffle has not yet touched. Together with the matching upper bound of Mission III, this exhibits the abrupt transition (cutoff) of the top-to-random shuffle at nlog⁡nn\log nnlogn.

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

/-- **Proposition 7.14** (LPW): for the top-to-random shuffle on `n` cards,
for every `ε > 0` there is an `α₀` such that for `α > α₀` and all
sufficiently large `n`, `d(n log n − α n) ≥ 1 − ε`. -/
theorem top_to_random_lower_bound (ε : ℝ) (hε : 0 < ε) :
    ∃ α₀ : ℝ, 0 < α₀ ∧ ∀ α : ℝ, α₀ < α → ∃ N : ℕ, ∀ n : ℕ, N ≤ n →
      ∀ t : ℕ, (t : ℝ) ≤ n * Real.log n - α * n →
        1 - ε ≤ distStationary (topToRandom n)
          (uniformDist (Equiv.Perm (Fin n))) t := 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 7.4.2, Proposition 7.14, pp. 96-97
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What the Lean code literally says, in plain math · claude-fable-5

For every real number ε>0\varepsilon > 0ε>0, the theorem asserts the existence of a real threshold α0\alpha_0α0​ with α0>0\alpha_0 > 0α0​>0 such that: for every real α\alphaα with α>α0\alpha > \alpha_0α>α0​ (strict), there exists a natural number NNN (which may depend on both ε\varepsilonε and α\alphaα) such that for every natural number n≥Nn \ge Nn≥N and every natural number t≥0t \ge 0t≥0 satisfying t≤nln⁡n−αnt \le n \ln n - \alpha nt≤nlnn−αn (natural logarithm; if nln⁡n−αn<0n \ln n - \alpha n < 0nlnn−αn<0 there is no such ttt and the claim is vacuous for that nnn), one has the non-strict inequality

1−ε  ≤  d(t)  :=  sup⁡x∈Sn  dTV(Pnt(x,⋅),  u).1 - \varepsilon \;\le\; d(t) \;:=\; \sup_{x \in S_n}\; d_{\mathrm{TV}}\bigl(P_n^t(x,\cdot),\; u\bigr).1−ε≤d(t):=x∈Sn​sup​dTV​(Pnt​(x,⋅),u).

Here the state space is SnS_nSn​, the group of permutations of an nnn-element set; uuu is the constant function assigning each permutation the value 1/n!1/n!1/n! (the reciprocal of the cardinality of SnS_nSn​); Pnt(x,⋅)P_n^t(x,\cdot)Pnt​(x,⋅) is row xxx of the ttt-th power of the transition matrix PnP_nPn​ (with Pn0=IP_n^0 = IPn0​=I); and for two real-valued functions μ,ν\mu,\nuμ,ν on a finite set, dTV(μ,ν)=sup⁡A∣∑σ∈Aμ(σ)−∑σ∈Aν(σ)∣d_{\mathrm{TV}}(\mu,\nu) = \sup_{A}\bigl|\sum_{\sigma \in A}\mu(\sigma) - \sum_{\sigma \in A}\nu(\sigma)\bigr|dTV​(μ,ν)=supA​​∑σ∈A​μ(σ)−∑σ∈A​ν(σ)​ is the supremum over all subsets AAA (no factor of 12\tfrac1221​, and a maximum since the set is finite). The matrix PnP_nPn​ is the top-to-random shuffle, defined concretely as follows: viewing a permutation xxx as an arrangement (position iii holds card x(i)x(i)x(i)), for each position j∈{0,…,n−1}j \in \{0,\dots,n-1\}j∈{0,…,n−1} the "insert" operation removes the top card x(0)x(0)x(0) and places it at position jjj, i.e. produces the arrangement yyy with y(i)=x(i+1)y(i) = x(i+1)y(i)=x(i+1) for i<ji < ji<j, y(j)=x(0)y(j) = x(0)y(j)=x(0), and y(i)=x(i)y(i) = x(i)y(i)=x(i) for i>ji > ji>j (so j=0j = 0j=0 returns xxx itself); then Pn(x,y)P_n(x,y)Pn​(x,y) is defined as 1n\frac{1}{n}n1​ times the number of positions jjj for which yyy coincides pointwise with the result of inserting at jjj. (For n=0n = 0n=0 this formula divides by zero, yielding the junk value 000; but such degenerate nnn matter only if N=0N = 0N=0 forces them into range, and the statement quantifies over all n≥Nn \ge Nn≥N for some existentially chosen NNN.) Note the quantifier structure carefully: α0\alpha_0α0​ is only claimed to exist and to be positive — nothing ties its size to ε\varepsilonε quantitatively — the conclusion is required only for α\alphaα strictly above α0\alpha_0α0​, only for nnn at least the (arbitrarily large) chosen NNN, and only for times ttt below nln⁡n−αnn \ln n - \alpha nnlnn−αn; the coefficient of the nln⁡nn \ln nnlnn term is 111 (not 12\tfrac1221​), and d(t)d(t)d(t) is a worst-case (supremum) over the starting permutation xxx.

Human review
  • Endorsed by Community (Bot) · Aug 21, 2026

  • Endorsed by Shuze Chen · Aug 21, 2026

    Confirmed by the mission captain (proposal self-audit).

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