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 approximateContinue reading “Quasi-Monte Carlo Software Article”

Visualizing the Internals of Object Classes in QMCPy

As a software library grows, so does its complexity. This comment certainly applies to QMCPy [1], our Python library for high-dimensional numerical integration. UML (Unified Modelling Language) diagrams are a helpful tool for visualizing QMCPy’s intricate object oriented framework. These network diagrams display an objects methods, attributes, dependencies, and inheritance relationships. We have used theContinue reading “Visualizing the Internals of Object Classes in QMCPy”

QMCPy Version 1.0

The developers of QMCPy are happy to announce the release of version 1.0 on February 12, 2021, Chinese New Year! We would like to thank all those who have made this development possible. A special thank you to Developers: Sou-Cheng T. Choi, Fred J. Hickernell, Michael McCourt, Jagadeeswaran Rathinavel, and Aleksei Sorokin; Collaborators: Mike Giles,Continue reading “QMCPy Version 1.0”

A QMCPy Quick Start

We have created QMCPy, an open-source, object-oriented quasi- Monte Carlo (QMC) software framework in Python 3, which contains standardized parent classes for modeling integrands, measure, discrete distribution, and stopping criteria.  We hope QMCPy could enable researchers and users to quickly extend and experiment with novel algorithmic components that validate their theories, or to simply leverage orContinue reading “A QMCPy Quick Start”