kriging.quantile.grad: Analytical gradient of the Kriging quantile of level beta

In DiceOptim: Kriging-Based Optimization for Computer Experiments

Description

Computes the gradient of the Kriging quantile of level beta at the current location. Only available for Universal Kriging with constant trend (Ordinary Kriging).

Usage

kriging.quantile.grad(x, model, beta = 0.1, type = "UK", envir = NULL)

Arguments

x	a vector representing the input for which one wishes to calculate kriging.quantile.grad.
model	an object of class km.
beta	A quantile level (between 0 and 1)
type	Kriging type: "SK" or "UK"
envir	environment for inheriting intermediate calculations from "kriging.quantile"

Value

The gradient of the Kriging mean predictor with respect to x. Returns 0 at design points (where the gradient does not exist).

Author(s)

Victor Picheny

David Ginsbourger

References

O. Roustant, D. Ginsbourger, Y. Deville, DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization, J. Stat. Soft., 2010. https://www.jstatsoft.org/article/view/v051i01

D. Ginsbourger (2009), Multiples metamodeles pour l'approximation et l'optimisation de fonctions numeriques multivariables, Ph.D. thesis, Ecole Nationale Superieure des Mines de Saint-Etienne, 2009.

See Also

EI.grad

Examples

##########################################################################
###    KRIGING QUANTILE SURFACE AND ITS GRADIENT FOR                  ####
###    THE BRANIN FUNCTION KNOWN AT A 12-POINT LATIN HYPERCUBE DESIGN ####
##########################################################################
set.seed(421)

# Set test problem parameters
doe.size <- 12
dim <- 2
test.function <- get("branin2")
lower <- rep(0,1,dim)
upper <- rep(1,1,dim)
noise.var <- 0.2

# Generate DOE and response
doe <- as.data.frame(matrix(runif(doe.size*dim),doe.size))
y.tilde <- rep(0, 1, doe.size)
for (i in 1:doe.size)  {
y.tilde[i] <- test.function(doe[i,]) + sqrt(noise.var)*rnorm(n=1)
}
y.tilde <- as.numeric(y.tilde)

# Create kriging model
model <- km(y~1, design=doe, response=data.frame(y=y.tilde),
        covtype="gauss", noise.var=rep(noise.var,1,doe.size), 
	lower=rep(.1,dim), upper=rep(1,dim), control=list(trace=FALSE))

# Compute actual function and criterion on a grid
n.grid <- 9 # Change to 21 for a nicer picture
x.grid <- y.grid <- seq(0,1,length=n.grid)
design.grid <- expand.grid(x.grid, y.grid)
nt <- nrow(design.grid)

crit.grid <- apply(design.grid, 1, kriging.quantile, model=model, beta=.1)
crit.grad <- t(apply(design.grid, 1, kriging.quantile.grad, model=model, beta=.1))

z.grid <- matrix(crit.grid, n.grid, n.grid)
contour(x.grid,y.grid, z.grid, 30)
title("kriging.quantile and its gradient")
points(model@X[,1],model@X[,2],pch=17,col="blue")

for (i in 1:nt)
{
 x <- design.grid[i,]
 arrows(x$Var1,x$Var2, x$Var1+crit.grad[i,1]*.01,x$Var2+crit.grad[i,2]*.01, 
length=0.04,code=2,col="orange",lwd=2)
}

DiceOptim documentation built on Feb. 2, 2021, 1:06 a.m.