Gaussian log-Sobolev bound for exponentials of Lipschitz functions:
ProvedGaussianMatrix.gaussian_lipschitz_entropy_boundLet be a finite index set and the standard Gaussian measure on . Let , and let satisfy
so that is -Lipschitz for the Euclidean distance. Then for every ,
that is, .
This is the Gaussian logarithmic Sobolev inequality (Gross, 1975) applied to . Here almost everywhere, by Rademacher's theorem, or after approximating 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 . If and , the hypothesis cannot hold. If , then is constant and the left-hand side is . The case is included and is equally true (replace by ).
import Definitions.Def_GaussianMatrix_basic open MeasureTheory ProbabilityTheory open scoped Matrix
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