Asymptotics for Optimal Empirical Quantization of Measures

We investigate the minimal error in approximating a general probability measure $\mu$ on $\mathbb{R}^d$ by the uniform measure on a finite set with prescribed cardinality $n$. The error is measured in the $p$-Wasserstein distance. In particular, when $1\le p<d$, we establish asymptotic upper and lower bounds as $n \to \infty$ on the rescaled minimal error that have the same, explicit dependency on $\mu$. In some instances, we prove that the rescaled minimal error has a limit. These include general measures in dimension $d = 2$ with $1 \le p < 2$, and uniform measures in arbitrary dimension with $1 \le p < d$. For some uniform measures, we prove the limit existence for $p \ge d$ as well. For a class of compactly supported measures with H\"older densities, we determine the convergence speed of the minimal error for every $p \ge 1$. Furthermore, we establish a new Pierce-type (i.e., nonasymptotic) upper estimate of the minimal error when $1 \le p < d$. In the initial sections, we survey the state of the art and draw connections with similar problems, such as classical and random quantization.