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arXiv · 2201.12324

Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein

Marco Cuturi·Laetitia Meng-Papaxanthos·Yingtao Tian·Charlotte Bunne·Geoff Davis·Olivier Teboul
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Abstract

Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms. The toolbox builds on various JAX features, such as automatic and custom reverse mode differentiation, vectorization, just-in-time compilation and accelerators support. The toolbox covers elementary computations, such as the resolution of the regularized OT problem, and more advanced extensions, such as barycenters, Gromov-Wasserstein, low-rank solvers, estimation of convex maps, differentiable generalizations of quantiles and ranks, and approximate OT between Gaussian mixtures. The toolbox code is available at \texttt{https://github.com/ott-jax/ott}

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Marco Cuturi, Laetitia Meng-Papaxanthos, Yingtao Tian, Charlotte Bunne, Geoff Davis, Olivier Teboul. 2022-01-28. Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein. https://arxiv.org/abs/2201.12324

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