A Metropolis random walk algorithm to estimate a lower bound of the star discrepancy
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Maryam Alsolami
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Michael Mascagni
Abstract
In this paper, we introduce a new algorithm for estimating the lower bounds for the star discrepancy of any arbitrary point sets in
Acknowledgements
The first author would like to express her thanks to the Saudi Arabian Cultural Mission (SACM) and Umm Al-Qura University (UQU) for the scholarship and support provided throughout this research. This study is part of her doctoral research in computer science at Florida State University (FSU). Also, the authors thank the Florida State University Research Computing Center, and Pittsburgh Supercomputing Center at the University of Pittsburgh and Carnegie Mellon University for their assistance with some of the time-consuming computations described here.
References
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- A time-step-robust algorithm to compute particle trajectories in 3-D unstructured meshes for Lagrangian stochastic methods
- Monte Carlo estimates of extremes of stationary/nonstationary Gaussian processes
- Two stochastic algorithms for solving elastostatics problems governed by the Lamé equation
- A Metropolis random walk algorithm to estimate a lower bound of the star discrepancy
- Linking the Monte Carlo radiative transfer algorithm to the radiative transfer equation
Artikel in diesem Heft
- Frontmatter
- A time-step-robust algorithm to compute particle trajectories in 3-D unstructured meshes for Lagrangian stochastic methods
- Monte Carlo estimates of extremes of stationary/nonstationary Gaussian processes
- Two stochastic algorithms for solving elastostatics problems governed by the Lamé equation
- A Metropolis random walk algorithm to estimate a lower bound of the star discrepancy
- Linking the Monte Carlo radiative transfer algorithm to the radiative transfer equation
