Poincar\'{e} and logarithmic Sobolev inequalities by decomposition of the energy landscape

TL;DR

This paper proves the Eyring-Kramers formula for the optimal constants in Poincaré and logarithmic Sobolev inequalities for diffusions in potential landscapes, using a refined two-scale approach and mean-difference estimates.

Contribution

It introduces a novel decomposition method of the energy landscape to derive sharp constants in PI and LSI for low-temperature diffusions.

Findings

Eyring-Kramers formula for PI and LSI constants established

Constants scale well in low-temperature regimes

Method captures metastable state dynamics

Abstract

We consider a diffusion on a potential landscape which is given by a smooth Hamiltonian $H:\mathbb {R}^n\to \mathbb {R}$ in the regime of low temperature $\varepsilon$. We proof the Eyring-Kramers formula for the optimal constant in the Poincar\'{e} (PI) and logarithmic Sobolev inequality (LSI) for the associated generator $L=\varepsilon \Delta -\nabla H\cdot\nabla$ of the diffusion. The proof is based on a refinement of the two-scale approach introduced by Grunewald et al. [Ann. Inst. Henri Poincar\'{e} Probab. Stat. 45 (2009) 302-351] and of the mean-difference estimate introduced by Chafa\"{i} and Malrieu [Ann. Inst. Henri Poincar\'{e} Probab. Stat. 46 (2010) 72-96]. The Eyring-Kramers formula follows as a simple corollary from two main ingredients: The first one shows that the PI and LSI constant of the diffusion restricted to metastable regions corresponding to the local minima scales well in $\varepsilon$. This mimics the fast convergence of the diffusion to metastable states. The second ingredient is the estimation of a mean-difference by a weighted transport distance. It contains the main contribution to the PI and LSI constant, resulting from exponentially long waiting times of jumps between metastable states of the diffusion.