Product rules are optimal for numerical integration in classical smoothness spaces
This provides sharp, dimension-independent error bounds for numerical integration in classical smoothness spaces, resolving the explicit constant dependence for practitioners.
The paper proves explicit error bounds for numerical integration of smooth functions on the d-dimensional unit cube, showing the optimal error order is min{1, d n^{-r/d}} with a constant depending only on r, not d. For n=m^d, tensor product rules achieve this optimal order.
We mainly study numerical integration of real valued functions defined on the $d$-dimensional unit cube with all partial derivatives up to some finite order $r\ge1$ bounded by one. It is well known that optimal algorithms that use $n$ function values achieve the error rate $n^{-r/d}$, where the hidden constant depends on $r$ and $d$. Here we prove explicit error bounds without hidden constants and, in particular, show that the optimal order of the error is $\min \bigl\{1, d \, n^{-r/d}\bigr\}$, where now the hidden constant only depends on $r$, not on $d$. For $n=m^d$, this optimal order can be achieved by (tensor) product rules. We also provide lower bounds for integration defined over an arbitrary open domain of volume one. We briefly discuss how lower bounds for integration may be applied for other problems such as multivariate approximation and optimization.