integration_design_cst {DiceOptim}  R Documentation 
Generic function to build integration points (for the SUR criterion)
Description
Modification of the function integration_design
from the package KrigInv
to
be usable for SURbased optimization with constraints.
Usage
integration_design_cst(
integcontrol = NULL,
lower,
upper,
model.fun = NULL,
model.constraint = NULL,
equality = FALSE,
critcontrol = NULL,
min.prob = 0.001
)
Arguments
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

Value
A list with components:
integration.points
p x d matrix of p points used for the numerical calculation of integralsintegration.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
Author(s)
Victor Picheny
Mickael Binois
References
Chevalier C., Picheny V., Ginsbourger D. (2012), The KrigInv package: An efficient and userfriendly R implementation of Krigingbased inversion algorithms, Computational Statistics and Data Analysis, 71, 10211034.
Chevalier C., Bect J., Ginsbourger D., Vazquez E., Picheny V., Richet Y. (2011), Fast parallel krigingbased stepwise uncertainty reduction with application to the identification of an excursion set, Technometrics, 56(4), 455465.
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, 787795.
See Also
crit_SUR_cst
KrigInv integration_design