.png){kind=icon}
Return
Effective discrepancy and numerical experiments
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cpc.2012.07.002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Many problems require the computation of a high dimensional integral, typically with a few tens of input factors, with a low number of integrand evaluations. To avoid the curse of dimensionality, we reduce the dimension before applying the Quasi-Monte Carlo method. We will show how to reduce the dimension by computing approximate Sobol indices of the variables with a two-levels fractional factorial design. Then, we will use the Sobol indices to define the effective discrepancy, which turns out to be correlated with the QMC error and thus enables one to choose a good sequence for the integral estimation. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Effective dimension
Discrepancy
Projection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.4
Papers:
1.2W
Citations:
3.7W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
ShareSave
View more