Description Usage Arguments Value Author(s) References See Also
View source: R/integration_design_cst.R
Modification of the function integration_design
from the package KrigInv
to
be usable for SUR-based optimization with constraints.
1 2 3 4 5 6 7 8 9 10 |
integcontrol |
Optional list specifying the procedure to build the integration points and weights.
Many options are possible. |
lower |
Vector containing the lower bounds of the design space. |
upper |
Vector containing the upper bounds of the design space. |
model.fun |
object of class |
model.constraint |
either one or a list of objects of class |
equality |
either |
critcontrol |
optional list of parameters (see |
min.prob |
This argument applies only when importance sampling distributions are chosen.
For numerical reasons we give a minimum probability for a point to
belong to the importance sample. This avoids probabilities equal to zero and importance sampling
weights equal to infinity. In an importance sample of M points, the maximum weight becomes
|
A list with components:
integration.points
p x d matrix of p points used for the numerical calculation of integrals
integration.weights
a vector of size p corresponding to the weight of each point. If all the points are equally
weighted, integration.weights is set to NULL
Victor Picheny
Mickael Binois
Chevalier C., Picheny V., Ginsbourger D. (2012), The KrigInv package: An efficient and user-friendly R implementation of Kriging-based inversion algorithms, Computational Statistics and Data Analysis, 71, 1021-1034.
Chevalier C., Bect J., Ginsbourger D., Vazquez E., Picheny V., Richet Y. (2011), Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set, Technometrics, 56(4), 455-465.
V. Picheny (2014), A stepwise uncertainty reduction approach to constrained global optimization, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics, JMLR W&CP 33, 787-795.
crit_SUR_cst
KrigInv integration_design
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