Gaussian logarithmic Sobolev inequality on : for functions
ProvedGaussianMatrix.gaussian_logsobolevLet be a finite index set and the standard Gaussian measure on . Let be continuously differentiable, with partial derivatives , and assume that , and are -integrable. Then
This is Gross's dimension-free logarithmic Sobolev inequality for the standard Gaussian measure. Applied to with it gives , 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). is ContDiff ℝ 1 g. With the integrability hypotheses every Bochner integral in the statement is a true integral. For , is constant and both sides are .
import Definitions.Def_GaussianMatrix_basic open MeasureTheory ProbabilityTheory open scoped Matrix
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