Linearization of Monge-Ampère Equations and Statistical Applications

Abstract

Optimal transport has found numerous applications across data science, many of which require differentiating the optimal transport map with respect to the underlying probability densities in the Fréchet sense. In this work, we show that when the reference measure $Q$ is sufficiently regular in space and the curve of target measures $\{P_t{\}}_{t\in I}$ is both spatially regular and ${\mathcal{C}}^1$ in time, then the associated curve of optimal transport maps $\{\nabla {\varphi }_t{\}}_{t\in I}$ pushing $Q$ toward $P_t$ is itself a ${\mathcal{C}}^1$ curve. Moreover, we identify its time derivative as the solution to the \emph{linearized Monge--Ampère equation}, a second-order elliptic PDE with strictly oblique boundary conditions and a vanishing zero-order term. Our proof relies on applying the implicit function theorem to the Monge--Ampère equation with natural boundary conditions. As consequences, we establish regularity of the transport-based quantile regressor with respect to the covariates and derive a central limit theorem for smooth optimal transport maps.