Dealing with moment measures via entropy and optimal transport
By Filippo Santambrogio. 2015
47 citations recorded by the source corpus.
A recent paper by Cordero-Erausquin and Klartag provides a characterization of the measures $\mu$ on $\R^d$ which can be expressed as the moment measures of suitable convex functions $u$, i.e. are of the form $(\nabla u)\_\\#e^{- u}$ for $u:\R^d\to\R\cup\{+\infty\}$ and finds the corresponding $u$ by a variational method in the class of convex functions. Here we propose a purely optimal-transport-based method to retrieve the same result. The variational problem becomes the minimization of an entropy and a transport cost among densities $\rho$ and the optimizer $\rho$ turns out to be $e^{-u}$. This requires to develop some estimates and some semicontinuity results for the corresponding functionals which are natural in optimal transport. The notion of displacement convexity plays a crucial role in the characterization and uniqueness of the minimizers.
Subfields: Applied Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/0d9ee8ac6751ce9f71ff1f5673513146bdad6ed1
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In Applied Mathematics
In Statistics and Probability
Citations in the corpus
- Stein kernels and moment maps (2018, background): “1 Moment maps In [12] (revisited in [35], and following earlier works [40, 14, 4, 27]), the following theorem was established: Theorem 2.”