Quasi-Monte Carlo methods for high-dimensional integration: the standard (weighted Hilbert space) setting and beyond

F. Y. Kuo, Ch. Schwab, I. H. Sloan


This paper is a contemporary review of quasi-Monte Carlo (QMC) methods, that is, equal-weight rules for the approximate evaluation of high-dimensional integrals over the unit cube \([0,1]^s\). It first introduces the by-now standard setting of weighted Hilbert spaces of functions with square-integrable mixed first derivatives, and then indicates alternative settings, such as non-Hilbert spaces, that can sometimes be more suitable. Original contributions include the extension of the fast component-by-component (CBC) construction of lattice rules that achieve the optimal convergence order (a rate of almost \(1/N\), where \(N\) is the number of points, independently of dimension) to so-called “product and order dependent” (POD) weights, as seen in some recent applications. Although the paper has a strong focus on lattice rules, the function space settings are applicable to all QMC methods. Furthermore, the error analysis and construction of lattice rules can be adapted to polynomial lattice rules from the family of digital nets.



quasi-Monte Carlo methods, QMC, high-dimensional integration, weighted spaces, reproducing kernel Hilbert spaces, Banach space settings, worst-case error, discrepancy, weighted Koksma–Hlawka inequality, lattice rules, low-discrepancy sequences

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DOI: http://dx.doi.org/10.21914/anziamj.v53i0.5230

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