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The tanh contraction lemma

Proved
MarkovMixing.ising_tanh_lemma

by Shuze Chen · Aug 22, 2026 · Mathlib 0df444a (Lean v4.33.1)

markov-chainsmixing-timesprobability

Fix an inverse temperature β>0\beta>0β>0 and consider the function

g(x)  =  tanh⁡(β(x+1))−tanh⁡(β(x−1)),g(x)\;=\;\tanh\bigl(\beta(x+1)\bigr)-\tanh\bigl(\beta(x-1)\bigr),g(x)=tanh(β(x+1))−tanh(β(x−1)),

which measures how much the conditional probability of a +++ spin under the Ising Glauber dynamics moves when the neighbourhood field increases from x−1x-1x−1 to x+1x+1x+1 (the heat-bath update at a site with neighbouring spin sum SSS chooses +++ with probability (1+tanh⁡(βS))/2(1+\tanh(\beta S))/2(1+tanh(βS))/2).

The theorem (Lemma 15.2 of Levin–Peres–Wilmer) asserts four elementary properties:

  1. ggg is even: g(−x)=g(x)g(-x)=g(x)g(−x)=g(x) for every real xxx;
  2. ggg is non-increasing on [0,∞)[0,\infty)[0,∞): 0≤x≤y0\le x\le y0≤x≤y implies g(y)≤g(x)g(y)\le g(x)g(y)≤g(x);
  3. g(x)≤2tanh⁡βg(x)\le 2\tanh\betag(x)≤2tanhβ for every real xxx — the value at x=0x=0x=0 is the global maximum;
  4. over odd integers the sharper bound holds: g(k)≤tanh⁡(2β)g(k)\le\tanh(2\beta)g(k)≤tanh(2β) for every odd k∈Zk\in\mathbb Zk∈Z — the value at k=±1k=\pm1k=±1 is the maximum among odd integers.

These four inequalities are the entire analytic content of the high-temperature theorem: in the one-site coupling of two adjacent configurations, the probability that the updated site disagrees is controlled by ggg evaluated at the neighbouring spin sum, which for a graph with all degrees even is an odd integer — whence the two conditions Δtanh⁡β<1\Delta\tanh\beta<1Δtanhβ<1 and (Δ/2)tanh⁡(2β)<1(\Delta/2)\tanh(2\beta)<1(Δ/2)tanh(2β)<1 of the goal theorem.

Preamble
import Definitions.Def_mm_ising
Formal statement
namespace MarkovMixing

/-- **Lemma 15.2** (LPW): the function
`ϕ(x) = tanh(β(x+1)) − tanh(β(x−1))` is even, is decreasing on `[0,∞)`,
is bounded by `ϕ(0) = 2 tanh β`, and on odd integers is bounded by
`ϕ(1) = tanh 2β`. -/
theorem ising_tanh_lemma (β : ℝ) (hβ : 0 < β) :
    (∀ x : ℝ, Real.tanh (β * (-x + 1)) - Real.tanh (β * (-x - 1)) =
      Real.tanh (β * (x + 1)) - Real.tanh (β * (x - 1))) ∧
    (∀ x y : ℝ, 0 ≤ x → x ≤ y →
      Real.tanh (β * (y + 1)) - Real.tanh (β * (y - 1)) ≤
        Real.tanh (β * (x + 1)) - Real.tanh (β * (x - 1))) ∧
    (∀ x : ℝ, Real.tanh (β * (x + 1)) - Real.tanh (β * (x - 1)) ≤
      2 * Real.tanh β) ∧
    ∀ k : ℤ, Odd k →
      Real.tanh (β * (k + 1)) - Real.tanh (β * (k - 1)) ≤
        Real.tanh (2 * β) := 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 15.1, Lemma 15.2, Eqs. (15.3)-(15.4), p. 202

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