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Gaussian logarithmic Sobolev inequality on Rι\mathbb{R}^\iotaRι: Entγι(g2)≤2∫∥∇g∥22 dγι\mathrm{Ent}_{\gamma_\iota}(g^2)\le 2\int\|\nabla g\|_2^2\,d\gamma_\iotaEntγι​​(g2)≤2∫∥∇g∥22​dγι​ for C1C^1C1 functions ggg

Proved
GaussianMatrix.gaussian_logsobolev

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

dimension-freeentropygaussiangradientlog-sobolev

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 g:Rι→Rg : \mathbb{R}^\iota \to \mathbb{R}g:Rι→R be continuously differentiable, with partial derivatives ∂ig(x)=Dg(x) ei\partial_i g(x) = Dg(x)\,e_i∂i​g(x)=Dg(x)ei​, and assume that g2g^2g2, g2log⁡g2g^2\log g^2g2logg2 and ∥∇g∥22=∑i∈ι(∂ig)2\|\nabla g\|_2^2 = \sum_{i\in\iota}(\partial_i g)^2∥∇g∥22​=∑i∈ι​(∂i​g)2 are γι\gamma_\iotaγι​-integrable. Then

∫g2log⁡g2 dγι−(∫g2 dγι)log⁡(∫g2 dγι)  ≤  2∫∑i∈ι(∂ig)2 dγι.\int g^2\log g^2\,d\gamma_\iota - \Big(\int g^2\,d\gamma_\iota\Big)\log\Big(\int g^2\,d\gamma_\iota\Big) \;\le\; 2\int \sum_{i\in\iota} (\partial_i g)^2\, d\gamma_\iota .∫g2logg2dγι​−(∫g2dγι​)log(∫g2dγι​)≤2∫i∈ι∑​(∂i​g)2dγι​.

This is Gross's dimension-free logarithmic Sobolev inequality for the standard Gaussian measure. Applied to g=esf/2g = e^{sf/2}g=esf/2 with ∥∇f∥2≤L\|\nabla f\|_2 \le L∥∇f∥2​≤L it gives Ent(esf)≤s2L22 Eesf\mathrm{Ent}(e^{sf}) \le \tfrac{s^2L^2}{2}\,\mathbb{E}e^{sf}Ent(esf)≤2s2L2​Eesf, the input of Herbst's argument for sharp Gaussian concentration.

Formalization Note. The partial derivative is fderiv ℝ g x (Pi.single i 1); the Euclidean norm of the gradient is written out as a sum of squares (the default norm on ι → ℝ is the sup norm, but fderiv does not depend on the choice of norm). C1C^1C1 is ContDiff ℝ 1 g. With the integrability hypotheses every Bochner integral in the statement is a true integral. For ι=∅\iota = \emptysetι=∅, ggg is constant and both sides are 000.

Preamble
import Definitions.Def_GaussianMatrix_basic

open MeasureTheory ProbabilityTheory
open scoped Matrix
Formal statement
namespace GaussianMatrix

theorem gaussian_logsobolev {ι : Type*} [Fintype ι] [DecidableEq ι] (g : (ι → ℝ) → ℝ)
    (hg : ContDiff ℝ 1 g)
    (hg2 : Integrable (fun x => g x ^ 2) (Measure.pi fun _ : ι => gaussianReal 0 1))
    (hglog : Integrable (fun x => g x ^ 2 * Real.log (g x ^ 2))
      (Measure.pi fun _ : ι => gaussianReal 0 1))
    (hdg : Integrable (fun x => ∑ i, fderiv ℝ g x (Pi.single i 1) ^ 2)
      (Measure.pi fun _ : ι => gaussianReal 0 1)) :
    ∫ x, g x ^ 2 * Real.log (g x ^ 2) ∂(Measure.pi fun _ : ι => gaussianReal 0 1)
      - (∫ x, g x ^ 2 ∂(Measure.pi fun _ : ι => gaussianReal 0 1))
        * Real.log (∫ x, g x ^ 2 ∂(Measure.pi fun _ : ι => gaussianReal 0 1))
      ≤ 2 * ∫ x, ∑ i, fderiv ℝ g x (Pi.single i 1) ^ 2
        ∂(Measure.pi fun _ : ι => gaussianReal 0 1) := by
  sorry

end GaussianMatrix
Source
L. Gross, Logarithmic Sobolev inequalities, Amer. J. Math. 97 (1975), 1061–1083; S. Boucheron, G. Lugosi, P. Massart, Concentration Inequalities (Oxford Univ. Press, 2013), Theorem 5.4 (Gaussian logarithmic Sobolev inequality); M. Ledoux, The Concentration of Measure Phenomenon (AMS, 2001), §5.1 / (5.1). Numbering is cited from memory.

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