Remarks on curvature in the transportation metric

TL;DR

This paper generalizes Calabi's classical result on hyperbolic affine hyperspheres, showing that solutions to a specific Monge-Ampère equation induce metrics with non-positive Bakry-Émery tensor, with applications to optimal transportation and probability theory.

Contribution

It extends Calabi's theorem to a broader class of solutions, establishing non-positivity of the Bakry-Émery tensor in a generalized framework for optimal transportation.

Findings

The metric measure space has a non-positive Bakry-Émery tensor.

Established a third-order uniform dimension-free a priori estimate for optimal transportation.

Applied the tensorial maximum principle to the weighted Laplacian of the Bakry-Émery tensor.

Abstract

According to a classical result of E.~Calabi any hyperbolic affine hypersphere endowed with its natural Hessian metric has a non-positive Ricci tensor. The affine hyperspheres can be described as the level sets of solutions to the "hyperbolic" toric K\"ahler-Einstein equation $e^{\Phi} = \det D^2 \Phi$ on proper convex cones. We prove a generalization of this theorem by showing that for every $\Phi$ solving this equation on a proper convex domain $\Omega$ the corresponding metric measure space $(D^2 \Phi, e^{\Phi}dx)$ has a non-positive Bakry-{\'E}mery tensor. Modifying the Calabi computations we obtain this result by applying the tensorial maximum principle to the weighted Laplacian of the Bakry-{\'E}mery tensor. Our computations are carried out in a generalized framework adapted to the optimal transportation problem for arbitrary target and source measures. For the optimal transportation of the log-concave probability measures we prove a third-order uniform dimension-free apriori estimate in the spirit of the second-order Caffarelli contraction theorem, which has numerous applications in probability theory.