On optimal matching problem for Gaussian samples with dimension d ≥ 3 – Jie-Xiang Zhu (Fudan University / Université de Toulouse)


Optimal matching problems are very classical in computer science, physics and mathematics. In this talk, we will discuss the related rates of convergence of empirical measures associated with n independent random points, whose common distribution is the normal Gaussian distribution in Euclidean space with dimension d ≥ 3. Our method is based on the PDE and mass transportation approach developed by L. Ambrosio, F. Stra and D. Trevisan.

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