max_infill_criterion {KrigInv} | R Documentation |
Optimizer for the infill criteria
Description
Optimization, of the chosen infill criterion (maximization or minimization, depending on the case)
Usage
max_infill_criterion(lower, upper, optimcontrol = NULL,
method, T, model, method.param = NULL)
Arguments
lower |
Vector containing the lower bounds of the design space. |
upper |
Vector containing the upper bounds of the design space. |
optimcontrol |
Optional list of control parameters for the optimization of the sampling criterion. The field |
method |
Criterion used for choosing observations: |
T |
Array containing one or several thresholds. The |
model |
A Kriging model of |
method.param |
Optional tolerance value (scalar). Default value is 1 for |
Value
A list with components:
par |
The best set of parameters found. |
value |
The value of the chosen criterion at par. |
allvalues |
If an optimization on a discrete set of points is chosen, the value of the criterion at all these points. |
Author(s)
Victor Picheny (INRA, Toulouse, France)
David Ginsbourger (IDIAP Martigny and University of Bern, Switzerland)
Clement Chevalier (University of Neuchatel, Switzerland)
References
Bect J., Ginsbourger D., Li L., Picheny V., Vazquez E. (2012), Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing vol. 22(3), pp 773-793
Picheny V., Ginsbourger D., Roustant O., Haftka R.T., (2010) Adaptive designs of experiments for accurate approximation of a target region, J. Mech. Des. vol. 132(7)
Bichon B.J., Eldred M.S., Swiler L.P., Mahadevan S., McFarland J.M. (2008) Efficient global reliability analysis for nonlinear implicit performance functions, AIAA Journal 46(10), pp 2459-2468
Ranjan P., Bingham D., Michailidis G. (2008) Sequential experiment design for contour estimation from complex computer codes Technometrics 50(4), pp 527-541
See Also
EGI
,ranjan_optim
,tmse_optim
,bichon_optim
,tsee_optim
Examples
#max_infill_criterion
set.seed(9)
N <- 20 #number of observations
T <- 80 #threshold
testfun <- branin
lower <- c(0,0)
upper <- c(1,1)
#a 20 points initial design
design <- data.frame( matrix(runif(2*N),ncol=2) )
response <- testfun(design)
#km object with matern3_2 covariance
#params estimated by ML from the observations
model <- km(formula=~., design = design,
response = response,covtype="matern3_2")
optimcontrol <- list(method="genoud",pop.size=50)
## Not run:
obj <- max_infill_criterion(lower=lower,upper=upper,optimcontrol=optimcontrol,
method="bichon",T=T,model=model)
obj$par;obj$value
new.model <- update(object=model,newX=obj$par,newy=testfun(obj$par),cov.reestim=TRUE)
par(mfrow=c(1,2))
print_uncertainty(model=model,T=T,type="pn",lower=lower,upper=upper,
cex.points=2.5,main="probability of excursion")
print_uncertainty(model=new.model,T=T,type="pn",lower=lower,upper=upper,
new.points=1,col.points.end="red",cex.points=2.5,main="updated probability of excursion")
## End(Not run)