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Entropy dissipation along the Ornstein–Uhlenbeck semigroup: ddt∫Ptflog⁡Ptf dγ=−∫((Ptf)′)2/Ptf dγ\frac{d}{dt}\int P_tf\log P_tf\,d\gamma=-\int ((P_tf)')^2/P_tf\,d\gammadtd​∫Pt​flogPt​fdγ=−∫((Pt​f)′)2/Pt​fdγ

Proved
GaussianMatrix.ou_entropy_hasDerivAt

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

entropyfisher-informationgaussianornstein-uhlenbecksemigroup

Let γ=N(0,1)\gamma=N(0,1)γ=N(0,1) be the standard Gaussian measure on R\mathbb{R}R. For s∈Rs\in\mathbb{R}s∈R and a function hhh, let

Psh(x)=∫h(e−sx+1−e−2s y) dγ(y)P_sh(x)=\int h\big(e^{-s}x+\sqrt{1-e^{-2s}}\,y\big)\,d\gamma(y)Ps​h(x)=∫h(e−sx+1−e−2s​y)dγ(y)

be the Ornstein–Uhlenbeck (Mehler) semigroup. Let δ>0\delta>0δ>0 and C∈RC\in\mathbb{R}C∈R, and let f∈C1(R)f\in C^1(\mathbb{R})f∈C1(R) satisfy δ≤f≤C\delta\le f\le Cδ≤f≤C and ∣f′∣≤C|f'|\le C∣f′∣≤C. Then for every t>0t>0t>0 the function s↦∫Psflog⁡Psf dγs\mapsto\int P_sf\log P_sf\,d\gammas↦∫Ps​flogPs​fdγ is differentiable at ttt, and

dds∣s=t∫Psflog⁡Psf dγ  =  −∫((Ptf)′)2Ptf dγ,\frac{d}{ds}\Big|_{s=t}\int P_sf\log P_sf\,d\gamma\;=\;-\int\frac{\big((P_tf)'\big)^2}{P_tf}\,d\gamma ,dsd​​s=t​∫Ps​flogPs​fdγ=−∫Pt​f((Pt​f)′)2​dγ,

where (Ptf)′(P_tf)'(Pt​f)′ is the derivative of x↦Ptf(x)x\mapsto P_tf(x)x↦Pt​f(x).

This is the de Bruijn-type identity behind the Bakry–Émery proof of the Gaussian log-Sobolev inequality: the entropy decreases with rate equal to the Fisher information. The proof has three ingredients:

  • differentiation under the integral sign;
  • the generator identity ∂tPtf=(Ptf)′′−x(Ptf)′\partial_tP_tf=(P_tf)''-x(P_tf)'∂t​Pt​f=(Pt​f)′′−x(Pt​f)′, valid for t>0t>0t>0 even though only f∈C1f\in C^1f∈C1 is assumed, because Gaussian integration by parts in the noise variable gives (Ptf)′′=e−2t1−e−2t∫y f′(e−tx+1−e−2t y) dγ(y)(P_tf)''=\frac{e^{-2t}}{\sqrt{1-e^{-2t}}}\int y\,f'(e^{-t}x+\sqrt{1-e^{-2t}}\,y)\,d\gamma(y)(Pt​f)′′=1−e−2t​e−2t​∫yf′(e−tx+1−e−2t​y)dγ(y);
  • Gaussian integration by parts in xxx.

Formalization Note. PsfP_sfPs​f is written out explicitly. The restriction t>0t>0t>0 is needed, since 1−e−2s\sqrt{1-e^{-2s}}1−e−2s​ is not differentiable at s=0s=0s=0.

Preamble
import Definitions.Def_GaussianMatrix_basic

open MeasureTheory ProbabilityTheory
open scoped Matrix
Formal statement
namespace GaussianMatrix

theorem ou_entropy_hasDerivAt (f : ℝ → ℝ) (hf : ContDiff ℝ 1 f) (δ C : ℝ) (hδ : 0 < δ)
    (hlow : ∀ x, δ ≤ f x) (hup : ∀ x, f x ≤ C) (hdf : ∀ x, |deriv f x| ≤ C) (t : ℝ)
    (ht : 0 < t) :
    HasDerivAt
      (fun s => ∫ x,
        (∫ y, f (Real.exp (-s) * x + Real.sqrt (1 - Real.exp (-(2 * s))) * y) ∂(gaussianReal 0 1))
          * Real.log (∫ y, f (Real.exp (-s) * x + Real.sqrt (1 - Real.exp (-(2 * s))) * y)
              ∂(gaussianReal 0 1)) ∂(gaussianReal 0 1))
      (-∫ x,
        deriv (fun z => ∫ y, f (Real.exp (-t) * z + Real.sqrt (1 - Real.exp (-(2 * t))) * y)
            ∂(gaussianReal 0 1)) x ^ 2
          / ∫ y, f (Real.exp (-t) * x + Real.sqrt (1 - Real.exp (-(2 * t))) * y) ∂(gaussianReal 0 1)
        ∂(gaussianReal 0 1)) t := by
  sorry

end GaussianMatrix
Source
D. Bakry, I. Gentil, M. Ledoux, Analysis and Geometry of Markov Diffusion Operators (Springer, 2014), §2.7.1 (Ornstein–Uhlenbeck semigroup, Mehler formula) and §5.7 (entropy dissipation ddtEnt(Ptf)=−I(Ptf)\frac{d}{dt}\mathrm{Ent}(P_tf)=-I(P_tf)dtd​Ent(Pt​f)=−I(Pt​f)); M. Ledoux, The Concentration of Measure Phenomenon (AMS, 2001), §5.1 (proof of Theorem 5.1). Section numbers are cited from memory.

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