Stochastic optimal transport in Banach Spaces for regularized estimation of multivariate quantiles

• URL: http://arxiv.org/abs/2302.00982v2
• Date: Mon, 19 Feb 2024 13:41:45 GMT
• Title: Stochastic optimal transport in Banach Spaces for regularized estimation of multivariate quantiles
• Authors: Bernard Bercu, J\'er\'emie Bigot and Gauthier Thurin
• Abstract summary: We introduce a new algorithm for solving entropic optimal transport (EOT) between two absolutely continuous probability measures $mu$ and $nu$. We study the almost sure convergence of our algorithm that takes its values in an infinite-dimensional Banach space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a new stochastic algorithm for solving entropic optimal transport (EOT) between two absolutely continuous probability measures $\mu$ and $\nu$. Our work is motivated by the specific setting of Monge-Kantorovich quantiles where the source measure $\mu$ is either the uniform distribution on the unit hypercube or the spherical uniform distribution. Using the knowledge of the source measure, we propose to parametrize a Kantorovich dual potential by its Fourier coefficients. In this way, each iteration of our stochastic algorithm reduces to two Fourier transforms that enables us to make use of the Fast Fourier Transform (FFT) in order to implement a fast numerical method to solve EOT. We study the almost sure convergence of our stochastic algorithm that takes its values in an infinite-dimensional Banach space. Then, using numerical experiments, we illustrate the performances of our approach on the computation of regularized Monge-Kantorovich quantiles. In particular, we investigate the potential benefits of entropic regularization for the smooth estimation of multivariate quantiles using data sampled from the target measure $\nu$.

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