Quasi-Monte Carlo Community Software

Quasi-Monte Carlo (QMC) methods are used to approximate multivariate integrals. They have four main components: an integrand, a discrete distribution, summary output data, and stopping criterion. Information about the integrand is obtained as a sequence of values of the function sampled at the data-sites of the discrete distribution. The stopping criterion tells the algorithm when the user-specified error tolerance has been satisfied. We are developing a framework that allows collaborators in the QMC community to develop plug-and-play modules in an effort to produce more efficient and portable QMC software. Each of the above four components is an abstract class. Abstract classes specify the common properties and methods of all subclasses. The ways in which the four kinds of classes interact with each other are also specified. Subclasses then flesh out different integrands, sampling schemes, and stopping criteria. Besides providing developers a way to link their new ideas with those implemented by the rest of the QMC community, we also aim to provide practitioners with state-of-the-art QMC software for their applications.

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Installation

pip install qmcpy

The QMCPy Framework

The central package including the 5 main components as listed below. Each component is implemented as abstract classes with concrete implementations. For example, the lattice and Sobol' sequences are implemented as concrete implementations of the DiscreteDistribution abstract class. A complete list of concrete implementations and thorough documentation can be found in the QMCPy Read the Docs.

• Stopping Criterion: determines the number of samples necessary to meet an error tolerance.
• Integrand: the function/process whose expected value will be approximated.
• True Measure: the distribution to be integrated over.
• Discrete Distribution: a generator of nodes/sequences that can be either IID (for Monte Carlo) or low-discrepancy (for quasi-Monte Carlo), that mimic a standard distribution.
• Accumulate Data: stores and updates data used in the integration process.

Quickstart

Note: If the following mathematics is not rendering try using Google Chrome and installing the Mathjax Plugin for GitHub.

We will approximate the expected value of the $d$ dimensional Keister integrand [18]

$$g(X)=\pi^{d/2}\cos(\lVert X \rVert)$$

where $X \sim \mathcal{N}(\boldsymbol{0},\boldsymbol{I}/2)$.

We may choose a Sobol' discrete distribution with a corresponding Sobol' cubature stopping criterion to preform quasi-Monte Carlo integration.

import qmcpy as qp
from numpy import pi, cos, sqrt, linalg
d = 2
dnb2 = qp.DigitalNetB2(d)
gauss_sobol = qp.Gaussian(dnb2, mean=0, covariance=1/2)
k = qp.CustomFun(
  true_measure = gauss_sobol, 
  g = lambda x: pi**(d/2)*cos(linalg.norm(x,axis=1)))
qmc_sobol_algorithm = qp.CubQMCSobolG(k, abs_tol=1e-3)
solution,data = qmc_sobol_algorithm.integrate()
print(data)

Running the above code outputs

LDTransformData (AccumulateData Object)
    solution        1.808
    error_bound     4.68e-04
    n_total         2^(13)
    time_integrate  0.008
CubQMCSobolG (StoppingCriterion Object)
    abs_tol         0.001
    rel_tol         0
    n_init          2^(10)
    n_max           2^(35)
CustomFun (Integrand Object)
Gaussian (TrueMeasure Object)
    mean            0
    covariance      2^(-1)
    decomp_type     PCA
Sobol (DiscreteDistribution Object)
    d               2^(1)
    dvec            [0 1]
    randomize       LMS_DS
    graycode        0
    entropy         127071403717453177593768120720330942628
    spawn_key       ()

A more detailed quickstart can be found in our GitHub repo at QMCSoftware/demos/quickstart.ipynb or in this Google Colab quickstart notebook.

We also highly recommend you take a look at Fred Hickernell's tutorial at the Monte Carlo Quasi-Monte Carlo 2020 Conference and the corresponding MCQMC2020 Google Colab notebook.

Community

Please refer to this document for the key roles in the QMCPy community.

Citation

If you find QMCPy helpful in your work, please support us by citing the following work:

Choi, S.-C. T., Hickernell, F. J., McCourt, M., Rathinavel, J. & Sorokin, A.
QMCPy: A quasi-Monte Carlo Python Library. Working. 2020.
https://qmcsoftware.github.io/QMCSoftware/

BibTex citation available here

Video Tutorial

Please refer to this video for a quick introduction to QMCPy.

For a more detail introduction refer to this video.

References

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[2] Fred J. Hickernell, Lan Jiang, Yuewei Liu, and Art B. Owen, "Guaranteed conservative fixed width confidence intervals via Monte Carlo sampling," Monte Carlo and Quasi-Monte Carlo Methods 2012 (J. Dick, F.Y. Kuo, G. W. Peters, and I. H. Sloan, eds.), pp. 105-128, Springer-Verlag, Berlin, 2014. DOI: 10.1007/978-3-642-41095-6_5

[3] Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell, Lan Jiang, Lluis Antoni Jimenez Rugama, Da Li, Jagadeeswaran Rathinavel, Xin Tong, Kan Zhang, Yizhi Zhang, and Xuan Zhou, GAIL: Guaranteed Automatic Integration Library (Version 2.3.1) [MATLAB Software], 2020. Available from http://gailgithub.github.io/GAIL_Dev/.

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[13] Faure, Henri, and Christiane Lemieux. “Implementation of Irreducible Sobol’ Sequences in Prime Power Bases,” Mathematics and Computers in Simulation 161 (2019): 13–22.

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[15] M. B. Giles. "Improved multilevel Monte Carlo convergence using the Milstein scheme," 343-358, in Monte Carlo and Quasi-Monte Carlo Methods 2006, Springer, 2008. http://people.maths.ox.ac.uk/~gilesm/files/mcqmc06.pdf.

[16] M. B. Giles and B. J. Waterhouse. "Multilevel quasi-Monte Carlo path simulation," pp.165-181 in Advanced Financial Modelling, in Radon Series on Computational and Applied Mathematics, de Gruyter, 2009. http://people.maths.ox.ac.uk/~gilesm/files/radon.pdf.

[17] Owen, A. B. "A randomized Halton algorithm in R," 2017. arXiv:1706.02808 [stat.CO]

[18] B. D. Keister, Multidimensional Quadrature Algorithms, 'Computers in Physics', 10, pp. 119-122, 1996.

[19] L’Ecuyer, Pierre & Munger, David. (2015). LatticeBuilder: A General Software Tool for Constructing Rank-1 Lattice Rules. ACM Transactions on Mathematical Software. 42. 10.1145/2754929.

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[23] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., … Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d extquotesingle Alch'e-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (pp. 8024–8035). Curran Associates, Inc. Retrieved from http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf

[24] S. Joe and F. Y. Kuo, Constructing Sobol sequences with better two-dimensional projections, SIAM J. Sci. Comput. 30, 2635-2654 (2008).

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Sponsors

• Illinois Tech

  

• Kamakura Corporation, acquired by SAS Institute Inc. in June 2022

  

• SigOpt, Inc.