Discretization on high-dimensional domains

Let $\mu$ be a Borel probability measure on a compact path-connected metric space $(X, \rho)$ for which there exist constants $c,\beta>1$ such that $\mu(B) \geq c r^{\beta}$ for every open ball $B\subset X$ of radius $r>0$. For a class of Lipschitz functions $\Phi:[0,\infty)\to R$ that piecewisely lie in a finite-dimensional subspace of continuous functions, we prove under certain mild conditions on the metric $\rho$ and the measure $\mu$ that for each positive integer $N\geq 2$, and each $g\in L^\infty(X, d\mu)$ with $|g|\infty=1$, there exist points $y1, \ldots, y{ N}\in X$ and real numbers $\lambda1, \ldots, \lambda{ N}$ such that for any $x\in X$, \begin{align*} & \left| \intX \Phi (\rho (x, y)) g(y) \,d \mu (y) - \sum{j = 1}^{ N} \lambdaj \Phi (\rho (x, y_j)) \right| \leq C N^{- \frac{1}{2} - \frac{3}{2\beta}} \sqrt{\log N}, \end{align*} where the constant $C>0$ is independent of $N$ and $g$. In the case when $X$ is the unit sphere $S^d$ of $R^{d+1}$ with the ususal geodesic distance, we also prove that the constant $C$ here is independent of the dimension $d$. Our estimates are better than those obtained from the standard Monte Carlo methods, which typically yield a weaker upper bound $N^{{-\frac12}\sqrt{\log} N}$.