Quasi-Monte Carlo Software Article

We recently uploaded an article on Quasi-Monte Carlo Software to https://arxiv.org/abs/2102.07833.

Abstract:

Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, 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 to approximate multivariate integrals or expectations of functions of vector random variables by QMC. 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.

Citation:

S.-C. T. Choi, F. J. Hickernell, R. Jagadeeswaran, M. J. McCourt, A. G. Sorokin. Quasi-Monte Carlo Software. 2020+. arXiv: 2102.07833 [cs.MS].

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Dr. Sou-Cheng T. Choi is Chief Data Scientist at the Kamakura Corporation and Research Associate Professor of Applied Mathematics at the Illinois Institute of Technology.

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Fred Hickernell is Professor of Applied Mathematics and Vice Provost for Research at Illinois Institute of Technology. His research spans computational mathematics and statistics.

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I work at SigOpt, a SF startup, developing and deploying Bayesian optimization tools for customers in finance, AI, consumer goods and general research and development.

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Aleksei Sorokin is an Applied Mathematics and Data Science student at the Illinois Institute of Technology.

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