max_sur_parallel {KrigInv} | R Documentation |
Minimizer of the parallel "sur"
or "jn"
criterion
Description
Minimization, based on the package rgenoud (or on exhaustive search on a discrete set), of the "sur"
or "jn"
criterion for a batch of candidate sampling points.
Usage
max_sur_parallel(lower, upper, optimcontrol = NULL,
batchsize, integration.param, T,
model, new.noise.var = 0,real.volume.variance=FALSE)
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 |
batchsize |
Number of points to sample simultaneously. The sampling criterion will return batchsize points at a time for sampling. |
integration.param |
Optional list of control parameter for the computation of integrals, containing the fields |
T |
Target value (scalar). |
model |
A Kriging model of |
new.noise.var |
Optional scalar value of the noise variance of the new observations. |
real.volume.variance |
Optional argument to use the |
Value
A list with components:
par |
the best set of points found. |
value |
the value of the sur 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)
Clement Chevalier (University of Neuchatel, Switzerland)
References
Chevalier C., Bect J., Ginsbourger D., Vazquez E., Picheny V., Richet Y. (2014), Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set, Technometrics, vol. 56(4), pp 455-465
Chevalier C., Ginsbourger D. (2014), Corrected Kriging update formulae for batch-sequential data assimilation, in Pardo-Iguzquiza, E., et al. (Eds.) Mathematics of Planet Earth, pp 119-122
See Also
EGIparallel
,sur_optim_parallel
,jn_optim_parallel
Examples
#max_sur_parallel
set.seed(9)
N <- 20 #number of observations
T <- c(40,80) #thresholds
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,optim.option=1)
integcontrol <- list(distrib="sur",n.points=50,init.distrib="MC")
integration.param <- integration_design(integcontrol=integcontrol,d=2,
lower=lower,upper=upper,model=model,
T=T)
batchsize <- 5 #number of new points
## Not run:
obj <- max_sur_parallel(lower=lower,upper=upper,optimcontrol=optimcontrol,
batchsize=batchsize,T=T,model=model,
integration.param=integration.param)
#one (hard) optim in dimension 5*2 !
obj$par;obj$value #optimum in 5 new points
new.model <- update(object=model,newX=obj$par,newy=apply(obj$par,1,testfun),
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=batchsize,col.points.end="red",cex.points=2.5,
main="updated probability of excursion")
## End(Not run)