We return to the problem of numerical integration of multivariate functions. As already mentioned in Sect.  1.1 we normalize the integration domain to be the compact unit cube \([0,1]^s\) , and hence the integrals considered are of the form ( 1.1 ). We aim at approximating such integrals by QMC rules of the form \(Q_{N,s}(f):=\frac{1}{N} \sum _{n=0}^{N-1} f(\boldsymbol{x}_n)\) with fixed integration nodes \(\boldsymbol{x}_0,\ldots ,\boldsymbol{x}_{N-1}\) taken from \([0,1)^s\) , i.e.: \(\begin{aligned} \int _{[0,1]^s} f(\boldsymbol{x})\,\textrm{d}\boldsymbol{x}\approx Q_{N,s}(f). \end{aligned}\) On QMC rulefirst sight this approach looks quite simple but the crux of this method is the choice of underlying nodes. On the other hand, as already mentioned, the knowledge of the integration nodes is insufficient for solving the integration problem in full generality.

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QMC Integration in Reproducing Kernel Hilbert Spaces

  • Gunther Leobacher,
  • Friedrich Pillichshammer

摘要

We return to the problem of numerical integration of multivariate functions. As already mentioned in Sect.  1.1 we normalize the integration domain to be the compact unit cube \([0,1]^s\) , and hence the integrals considered are of the form ( 1.1 ). We aim at approximating such integrals by QMC rules of the form \(Q_{N,s}(f):=\frac{1}{N} \sum _{n=0}^{N-1} f(\boldsymbol{x}_n)\) with fixed integration nodes \(\boldsymbol{x}_0,\ldots ,\boldsymbol{x}_{N-1}\) taken from \([0,1)^s\) , i.e.: \(\begin{aligned} \int _{[0,1]^s} f(\boldsymbol{x})\,\textrm{d}\boldsymbol{x}\approx Q_{N,s}(f). \end{aligned}\) On QMC rulefirst sight this approach looks quite simple but the crux of this method is the choice of underlying nodes. On the other hand, as already mentioned, the knowledge of the integration nodes is insufficient for solving the integration problem in full generality.