Lower Bounds for the Error of Quadrature Formulas for Hilbert Spaces

We prove lower bounds for the worst case error of quadrature formulas that use given sample points ${\mathcal X}_n = \{ x_1, \dots , x_n \}$. We are mainly interested in optimal point sets ${\mathcal X}_n$, but also prove lower bounds that hold for most randomly selected sets. As a tool, we use a recent result (and extensions thereof) of Vybiral on the positive semi-definiteness of certain matrices related to the product theorem of Schur. The new technique also works for spaces of analytic functions where known methods based on decomposable kernels cannot be applied.

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