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Gaussian log-Sobolev bound for exponentials of Lipschitz functions: Entγ(esf)≤s2L22 Eγesf\mathrm{Ent}_\gamma(e^{sf})\le\frac{s^2L^2}{2}\,\mathbb{E}_\gamma e^{sf}Entγ​(esf)≤2s2L2​Eγ​esf

Proved
GaussianMatrix.gaussian_lipschitz_entropy_bound

by tc · Oct 9, 2026 · Mathlib 0df444a (Lean v4.33.1)

concentration-of-measureentropygaussian-measurelipschitz-functionslog-sobolev-inequality

Let ι\iotaι be a finite index set and γι=⨂i∈ιN(0,1)\gamma_\iota = \bigotimes_{i\in\iota} N(0,1)γι​=⨂i∈ι​N(0,1) the standard Gaussian measure on Rι\mathbb{R}^\iotaRι. Let L∈RL \in \mathbb{R}L∈R, and let f:Rι→Rf : \mathbb{R}^\iota \to \mathbb{R}f:Rι→R satisfy

∣f(x)−f(y)∣≤L(∑i∈ι(xi−yi)2)1/2for all x,y,|f(x) - f(y)| \le L\Big(\sum_{i\in\iota}(x_i - y_i)^2\Big)^{1/2} \qquad \text{for all } x, y,∣f(x)−f(y)∣≤L(i∈ι∑​(xi​−yi​)2)1/2for all x,y,

so that fff is LLL-Lipschitz for the Euclidean distance. Then for every s∈Rs \in \mathbb{R}s∈R,

∫sf esf dγι−(∫esf dγι)log⁡(∫esf dγι)  ≤  s2L22∫esf dγι,\int s f\, e^{sf}\, d\gamma_\iota - \Big(\int e^{sf}\, d\gamma_\iota\Big)\log\Big(\int e^{sf}\, d\gamma_\iota\Big) \;\le\; \frac{s^2L^2}{2}\int e^{sf}\, d\gamma_\iota ,∫sfesfdγι​−(∫esfdγι​)log(∫esfdγι​)≤2s2L2​∫esfdγι​,

that is, Entγι(esf)≤s2L22 Eγιesf\mathrm{Ent}_{\gamma_\iota}(e^{sf}) \le \tfrac{s^2L^2}{2}\,\mathbb{E}_{\gamma_\iota} e^{sf}Entγι​​(esf)≤2s2L2​Eγι​​esf.

This is the Gaussian logarithmic Sobolev inequality Entγ(g2)≤2∫∥∇g∥2 dγ\mathrm{Ent}_\gamma(g^2) \le 2\int \|\nabla g\|^2\, d\gammaEntγ​(g2)≤2∫∥∇g∥2dγ (Gross, 1975) applied to g=esf/2g = e^{sf/2}g=esf/2. Here ∥∇g∥2≤s2L24esf\|\nabla g\|^2 \le \tfrac{s^2L^2}{4} e^{sf}∥∇g∥2≤4s2L2​esf almost everywhere, by Rademacher's theorem, or after approximating fff by smooth Lipschitz functions. It is the analytic input of Herbst's argument, which together with the Chernoff bound gives the sharp Gaussian concentration inequality.

Formalization Note. Every integral in the statement is finite, because Lipschitz functions have Gaussian exponential moments of all orders. The hypothesis allows any real LLL. If L<0L < 0L<0 and ι≠∅\iota \neq \emptysetι=∅, the hypothesis cannot hold. If ι=∅\iota = \emptysetι=∅, then fff is constant and the left-hand side is 000. The case s≤0s \le 0s≤0 is included and is equally true (replace fff by −f-f−f).

Preamble
import Definitions.Def_GaussianMatrix_basic

open MeasureTheory ProbabilityTheory
open scoped Matrix
Formal statement
namespace GaussianMatrix

theorem gaussian_lipschitz_entropy_bound {ι : Type*} [Fintype ι] (f : (ι → ℝ) → ℝ) (L : ℝ)
    (hLip : ∀ x y, |f x - f y| ≤ L * Real.sqrt (∑ i, (x i - y i) ^ 2)) (s : ℝ) :
    ∫ x, s * f x * Real.exp (s * f x) ∂(Measure.pi fun _ : ι => gaussianReal 0 1)
      - (∫ x, Real.exp (s * f x) ∂(Measure.pi fun _ : ι => gaussianReal 0 1))
        * Real.log (∫ x, Real.exp (s * f x) ∂(Measure.pi fun _ : ι => gaussianReal 0 1))
      ≤ s ^ 2 * L ^ 2 / 2 * ∫ x, Real.exp (s * f x) ∂(Measure.pi fun _ : ι => gaussianReal 0 1) := by
  sorry

end GaussianMatrix
Source
L. Gross, Logarithmic Sobolev inequalities, Amer. J. Math. 97 (1975); S. Boucheron, G. Lugosi, P. Massart, Concentration Inequalities (Oxford Univ. Press, 2013), Theorem 5.4 (Gaussian logarithmic Sobolev inequality) and the proof of Theorem 5.6, where it is applied to g=eλf/2g = e^{\lambda f/2}g=eλf/2; M. Ledoux, The Concentration of Measure Phenomenon (AMS, 2001), Chapter 5. Numbering is cited from memory.

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