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Quasi-Monte Carlo Software

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Monte Carlo and Quasi-Monte Carlo Methods (MCQMC 2020)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 387))

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Abstract

Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, robust, well-written software. This article, based on our MCQMC 2020 tutorial, describes some of the better quasi-Monte Carlo (QMC) software available. We highlight the key software components required by QMC to approximate multivariate integrals or expectations of functions of vector random variables. We have combined these components in QMCPy, a Python open-source library, which we hope will draw the support of the QMC community. Here we introduce QMCPy.

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Notes

  1. 1.

    QMCPy is in active development. This article is based on version 1.2 on PyPI.

  2. 2.

    The operator \(\oplus \) is commonly used to denote exclusive-or, which is its meaning for digital sequences in base 2. However, we are using it here in a more general sense.

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Acknowledgements

The authors would like to thank the organizers for a wonderful MCQMC 2020. We also thank the referees for their many helpful suggestions. This work is supported in part by SigOpt and National Science Foundation grant DMS-1522687.

Author information

Authors and Affiliations

  1. Kamakura Corporation, 2222 Kalakaua Ave, Suite 1400, Honolulu, HI, 96815, USA

    Sou-Cheng T. Choi

  2. Department of Applied Mathematics, Illinois Institute of Technology, RE 220, 10 W. 32nd St., Chicago, IL, 60616, USA

    Sou-Cheng T. Choi, Fred J. Hickernell, Rathinavel Jagadeeswaran & Aleksei G. Sorokin

  3. Center for Interdisciplinary Scientific Computation and Department of Applied Mathematics, Illinois Institute of Technology, RE 220, 10 W. 32nd St., Chicago, IL, 60616, USA

    Fred J. Hickernell

  4. Wi-Tronix LLC, 631 E Boughton Rd, Suite 240, Bolingbrook, IL, 60440, USA

    Rathinavel Jagadeeswaran

  5. SigOpt, an Intel company, 100 Bush St., Suite 1100, San Francisco, CA, 94104, USA

    Michael J. McCourt

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  1. Sou-Cheng T. Choi
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  2. Fred J. Hickernell
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  3. Rathinavel Jagadeeswaran
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  5. Aleksei G. Sorokin
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Correspondence to Fred J. Hickernell .

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  1. NVIDIA, Berlin, Germany

    Alexander Keller

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Choi, SC.T., Hickernell, F.J., Jagadeeswaran, R., McCourt, M.J., Sorokin, A.G. (2022). Quasi-Monte Carlo Software. In: Keller, A. (eds) Monte Carlo and Quasi-Monte Carlo Methods. MCQMC 2020. Springer Proceedings in Mathematics & Statistics, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-98319-2_2

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