The curse of dimensionality for numerical integration on general domains.

We prove the curse of dimensionality in the worst case setting for multivariate numerical integration for various classes of smooth functions. We prove the results when the domains are isotropic convex bodies with small diameter satisfying a universal ψ2-estimate. In particular, we obtain the result for the important class of volume-normalized ℓpd-balls in the complete regime 2≤p≤∞. This extends a result in a work of Hinrichs et al. (2014) to the whole range 2≤p≤∞, and additionally provides a unified approach. The key ingredient in the proof is a deep result from the theory of Asymptotic Geometric Analysis, the thin-shell volume concentration estimate due to O. Guédon and E. Milman. The connection of Asymptotic Geometric Analysis and Information-based Complexity revealed in this work seems promising and is of independent interest.