تقرير
Adaptive Multidimensional Integration: VEGAS Enhanced
| العنوان: | Adaptive Multidimensional Integration: VEGAS Enhanced |
|---|---|
| المؤلفون: | Lepage, G. Peter |
| المصدر: | Journal of Computational Physics 439 (2021) 110386 |
| سنة النشر: | 2020 |
| المجموعة: | High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics, High Energy Physics - Phenomenology |
| الوصف: | We describe a new algorithm, VEGAS+, for adaptive multidimensional Monte Carlo integration. The new algorithm adds a second adaptive strategy, adaptive stratified sampling, to the adaptive importance sampling that is the basis for its widely used predecessor VEGAS. Both VEGAS and VEGAS+ are effective for integrands with large peaks, but VEGAS+ can be much more effective for integrands with multiple peaks or other significant structures aligned with diagonals of the integration volume. We give examples where VEGAS+ is 2-19 times more accurate than VEGAS. We also show how to combine VEGAS+ with other integrators, such as the widely available MISER algorithm, to make new hybrid integrators. For a different kind of hybrid, we show how to use integrand samples, generated using MCMC or other methods, to optimize VEGAS+ before integrating. We give an example where preconditioned VEGAS+ is more than 100 times as efficient as VEGAS+ without preconditio ing. Finally, we give examples where VEGAS+ is more than 10 times as efficient as MCMC for Bayesian integrals with D = 3 and 21 parameters. We explain why VEGAS+ will often outperform MCMC for small and moderate sized problems. Comment: 23 pages, 11 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.jcp.2021.110386 |
| URL الوصول: | http://arxiv.org/abs/2009.05112 |
| رقم الأكسشن: | edsarx.2009.05112 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.jcp.2021.110386 |
|---|