# Stability of the logarithmic Sobolev inequality via the F\"ollmer Process

## TL;DR

This paper investigates the stability of the Gaussian logarithmic Sobolev inequality, establishing new bounds and counterexamples, and introduces a novel stability notion based on proximity to Gaussian mixtures, using stochastic methods.

## Contribution

The paper provides improved, dimension-free stability bounds for the inequality under covariance constraints and introduces a new stability concept related to Gaussian mixtures, with rigorous proofs.

## Key findings

- Dimension-free stability bounds when covariance is bounded by the identity.
- Counterexamples showing instability without covariance bounds.
- New stability estimates based on proximity to Gaussian mixtures.

## Abstract

We study the stability and instability of the Gaussian logarithmic Sobolev inequality, in terms of covariance, Wasserstein distance and Fisher information, addressing several open questions in the literature. We first establish an improved logarithmic Sobolev inequality which is at the same time scale invariant and dimension free. As a corollary, we show that if the covariance of the measure is bounded by the identity, one may obtain a sharp and dimension-free stability bound in terms of the Fisher information matrix. We then investigate under what conditions stability estimates control the covariance, and when such control is impossible. For the class of measures whose covariance matrix is dominated by the identity, we obtain optimal dimension-free stability bounds which show that the deficit in the logarithmic Sobolev inequality is minimized by Gaussian measures, under a fixed covariance constraint. On the other hand, we construct examples showing that without the boundedness of the covariance, the inequality is not stable. Finally, we study stability in terms of the Wasserstein distance, and show that even for the class of measures with a bounded covariance matrix, it is hopeless to obtain a dimension-free stability result. The counterexamples provided motivate us to put forth a new notion of stability, in terms of proximity to mixtures of the Gaussian distribution. We prove new estimates (some dimension-free) based on this notion. These estimates are strictly stronger than some of the existing stability results in terms of the Wasserstein metric. Our proof techniques rely heavily on stochastic methods.