A Dimension-Free Bound on the Poincaré Constant of Isotropic Log-Concave Measures
Abstract
The Kannan--Lovász--Simonovits (KLS) conjecture asserts that isotropic log-concave probability measures have Poincaré constants bounded by a universal constant, independently of dimension. We give a deterministic variational proof with an explicit bound on the Poincaré constant CP(μ)≤25, where μ is any isotropic log-concave probability measure. Starting from elliptic moment estimates and a quadratic variance inequality, we establish geometric bounds on normalized Appell coefficient norms through \emph{joint} maximization over the measure and test function. Variation of the measure gives a maximum-principle inequality, while stationarity in the test function controls the highest-order cumulant terms. Two concave barriers constructed from quadratic and cubic polynomials close the induction. A weighted Helmholtz--Hodge decomposition then controls the curl correction of weighted divergence, yielding a curvature estimate for compatible symmetric tensor fields that is uniform in rank. Quadratic duality converts this estimate into an operator comparison for centered integration, linking the coefficient bounds to control of the inverse gradient. A spectral-radius estimate and a scalar growth inequality for adjoint iterates then yield the Poincaré bound. The final estimate is independent of the auxiliary positive curvature, allowing approximation to complete the proof for general isotropic log-concave measures.
Related
- A dimension-free bound for isoperimetry of unconditional log-concave measures
- Exact bounds on the distribution function of isotropic log-concave distributions
- Convergence of Coordinate Ascent Variational Inference for log-concave measures via optimal transport
- Degree-Free Spectral Independence for Log-Concave Holant Measures
- Centro-sectional measures for log-concave functions
BibTeX
@misc{balasubramanian2026dimensionfree,
title = {{A Dimension-Free Bound on the Poincaré Constant of Isotropic Log-Concave Measures}},
author = {Krishnakumar Balasubramanian and Shiva Kasiviswanathan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07728},
eprint = {2610.07728},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07728},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Dimension-Free Bound on the Poincaré Constant of Isotropic Log-Concave Measures
AU - Krishnakumar Balasubramanian
AU - Shiva Kasiviswanathan
PY - 2026
DA - 2026/10/08
PB - arXiv
DO - 10.48550/arxiv.2610.07728
UR - https://arxiv.org/abs/2610.07728
AB - The Kannan--Lovász--Simonovits (KLS) conjecture asserts that isotropic log-concave probability measures have Poincaré constants bounded by a universal constant, independently of dimension. We give a deterministic variational proof with an explicit bound on the Poincaré constant C_P(μ)≤25, where μ is any isotropic log-concave probability measure. Starting from elliptic moment estimates and a quadratic variance inequality, we establish geometric bounds on normalized Appell coefficient norms through \emph{joint} maximization over the measure and test function. Variation of the measure gives a maximum-principle inequality, while stationarity in the test function controls the highest-order cumulant terms. Two concave barriers constructed from quadratic and cubic polynomials close the induction. A weighted Helmholtz--Hodge decomposition then controls the curl correction of weighted divergence, yielding a curvature estimate for compatible symmetric tensor fields that is uniform in rank. Quadratic duality converts this estimate into an operator comparison for centered integration, linking the coefficient bounds to control of the inverse gradient. A spectral-radius estimate and a scalar growth inequality for adjoint iterates then yield the Poincaré bound. The final estimate is independent of the auxiliary positive curvature, allowing approximation to complete the proof for general isotropic log-concave measures.
ER -