Hypercontractivity Meets Random Convex Hulls: Analysis of Randomized Multivariate Cubatures

Given a probability measure $\mu$ on a set $\mathcal{X}$ and a vector-valued function $\phi$, a common problem is to construct a discrete probability measure on $\mathcal{X}$ such that the push-forward of these two probability measures under $\phi$ is the same. This construction is at the heart of numerical integration methods that run under various names such as quadrature, cubature, or recombination. A natural approach is to sample points from $\mu$ until their convex hull of their image under $\phi$ includes the mean of $\phi$. Here we analyze the computational complexity of this approach when $\phi$ exhibits a graded structure by using so-called hypercontractivity. The resulting theorem not only covers the classical cubature case of multivariate polynomials, but also integration on pathspace, as well as kernel quadrature for product measures.