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The tent transformation can improve the convergence rate of quasi-Monte Carlo algorithms using digital nets

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Abstract

In this paper we investigate multivariate integration in reproducing kernel Sobolev spaces for which the second partial derivatives are square integrable. As quadrature points for our quasi-Monte Carlo algorithm we use digital (t,m,s)-nets over \(\mathbb{Z}_2\) which are randomly digitally shifted and then folded using the tent transformation. For this QMC algorithm we show that the root mean square worst-case error converges with order \(2^{m(-2+\varepsilon)}\) for any ɛ >  0, where 2m is the number of points. A similar result for lattice rules has previously been shown by Hickernell.

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Authors and Affiliations

  1. Institut für Finanzmathematik, Universität Linz, Altenbergstraße 69, 4040, Linz, Austria

    Ligia L. Cristea, Gunther Leobacher & Friedrich Pillichshammer

  2. Division of Engineering, Science & Technology, UNSW Asia, 1 Kay Siang Road, Singapore, 248922, Singapore

    Josef Dick

Authors
  1. Ligia L. Cristea
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  2. Josef Dick
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  3. Gunther Leobacher
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  4. Friedrich Pillichshammer
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Corresponding author

Correspondence to Josef Dick.

Additional information

Ligia L. Cristea is supported by the Austrian Research Fund (FWF), Project P 17022-N 12 and Project S 9609.

Josef Dick is supported by the Australian Research Council under its Center of Excellence Program.

Gunther Leobacher is supported by the Austrian Research Fund (FWF), Project S 8305.

Friedrich Pillichshammer is supported by the Austrian Research Fund (FWF), Project P 17022-N 12, Project S 8305 and Project S 9609.

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Cristea, L.L., Dick, J., Leobacher, G. et al. The tent transformation can improve the convergence rate of quasi-Monte Carlo algorithms using digital nets. Numer. Math. 105, 413–455 (2007). https://doi.org/10.1007/s00211-006-0046-x

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  • DOI: https://doi.org/10.1007/s00211-006-0046-x

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