Dimension-independent functional inequalities by tensorization and projection arguments
Probability
2025-01-03 v2 Analysis of PDEs
Functional Analysis
Abstract
We study stability under tensorization and projection-type operations of gradient-type estimates and other functional inequalities for Markov semigroups on metric spaces. Using transportation-type inequalities obtained by F. Baudoin and N. Eldredge in 2021, we prove that constants in the gradient estimates can be chosen to be independent of the dimension. Our results are applicable to hypoelliptic diffusions on sub-Riemannian manifolds and some hypocoercive diffusions. As a byproduct, we obtain dimension-independent reverse Poincar\'{e}, reverse logarithmic Sobolev, and gradient bounds for Lie groups with a transverse symmetry and for non-isotropic Heisenberg groups.
Cite
@article{arxiv.2403.18799,
title = {Dimension-independent functional inequalities by tensorization and projection arguments},
author = {Fabrice Baudoin and Maria Gordina and Rohan Sarkar},
journal= {arXiv preprint arXiv:2403.18799},
year = {2025}
}
Comments
33 pages, minor corrections, new examples added