View source: R/max_sur_parallel.R
max_sur_parallel | R Documentation |
"sur"
or "jn"
criterionMinimization, 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.
max_sur_parallel(lower, upper, optimcontrol = NULL, batchsize, integration.param, T, model, new.noise.var = 0,real.volume.variance=FALSE)
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 |
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. |
Clement Chevalier (University of Neuchatel, Switzerland)
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
EGIparallel
,sur_optim_parallel
,jn_optim_parallel
#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)
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