Our team is excited to announce that we will be presenting on QMCPy at the following upcoming events SAMO 2022: The Tenth International Conference on Sensitivity of Model Output. March 14 – 16, 2022. 2022 CORS/INFORMS International Conference. June 5 – 8, 2022. MCQMC 2022: The 15th International Conference on Monte Carlo and Quasi-Monte CarloContinue reading “QMCPy Events Coming Soon”
A Talk at the Great Lakes SIAM Conference
Aleksei Sorokin gave a virtual talk at the GLSIAM 2021 conference.
A Presentation at IIT’s Computational Mathematics Seminar
Aleksei Sorokin gave a talk as part of the Computational Mathematics Seminar Series at the Illinois Institute of Technology (IIT) .
A Talk at the Chicago Area SIAM Student Conference
Aleksei Sorokin gave a virtual talk at CASSC 2020.
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 , 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”
Speeding up QMCPy with Distributable C Code
Many Python packages rely on underlying C or C++ code to speed up their numerical methods. For example, NumPy calls C and C++ extensions in order to speed up matrix manipulation algorithms. Real Python’s article Python Bindings: Calling C or C++ From Python discusses a few reasons why you may want to utilize C or C++Continue reading “Speeding up QMCPy with Distributable C Code”
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 Seminar at PyData Chicago
Aleksei Sorokin gave a virtual talk at the PyData Chicago conference on August 27, 2020. More information on this talk is available here.
A Collection of QMCPy Posters
The QMCPy team has developed posters for conferences and events promoting scientific computation. The following posters highlight uses and features of the QMCPy package. CAURS 2021 Poster Session SIAM CSE 2021 Poster Session Applied math, Biology, Chemistry, Physics Back to School Poster Session. Illinois Institute of Technology, August 2020. Menger Day Poster Session. IllinoisContinue reading “A Collection of QMCPy Posters”
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”