Fully symmetric kernel quadrature

Toni Karvonen, Simo Särkkä

Research output: Contribution to journalArticleScientificpeer-review

14 Citations (Scopus)
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Kernel quadratures and other kernel-based approximation methods typically suffer from prohibitive cubic time and quadratic space complexity in the number of function evaluations. The problem arises because a system of linear equations needs to be solved. In this article we show that the weights of a kernel quadrature rule can be computed efficiently and exactly for up to tens of millions of nodes if the kernel, integration domain, and measure are fully symmetric and the node set is a union of fully symmetric sets. This is based on the observations that in such a setting there are only as many distinct weights as there are fully symmetric sets and that these weights can be solved from a linear system of equations constructed out of row sums of certain submatrices of the full kernel matrix. We present several numerical examples that show feasibility, both for a large number of nodes and in high dimensions, of the developed fully symmetric kernel quadrature rules. Most prominent of the fully symmetric kernel quadrature rules we propose are those that use sparse grids.
Original languageEnglish
Pages (from-to)A697-A720
Number of pages24
JournalSIAM Journal on Scientific Computing
Issue number2
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • numerical integration
  • kernel quadrature
  • Bayesian quadrature
  • reproducing kernel
  • Hilbert spaces
  • fully symmetric sets
  • sparse grids


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