DiceOptim (version 2.0)

max_EQI: Maximizer of the Expected Quantile Improvement criterion function

Description

Maximization, based on the package rgenoud of the Expected Quantile Improvement (EQI) criterion.

Usage

max_EQI(model, new.noise.var = 0, beta = 0.9, q.min = NULL, type = "UK", lower, upper, parinit = NULL, control = NULL)

Arguments

model

a Kriging model of "km" class

new.noise.var

the (scalar) noise variance of an observation. Default value is 0 (noise-free observation).

beta

Quantile level (default value is 0.9)

q.min

The current best kriging quantile. If not provided, this quantity is evaluated inside the EQI function (may increase computational time).

type

Kriging type: "SK" or "UK"

lower

vector containing the lower bounds of the variables to be optimized over

upper

optional vector containing the upper bounds of the variables to be optimized over

parinit

optional vector containing the initial values for the variables to be optimized over

control

optional list of control parameters for optimization. One can control "pop.size" (default : [N=3*2^dim for dim<6 and="" n="32*dim" otherwise]),="" "max.generations" (12), "wait.generations" (2) and "BFGSburnin" (2) of function "genoud" (see genoud). Numbers into brackets are the default values

Value

A list with components: A list with components:

Examples

set.seed(10)

# Set test problem parameters
doe.size <- 10
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))

# Optimisation using max_EQI
res <- max_EQI(model, new.noise.var=noise.var, type = "UK",
lower=c(0,0), upper=c(1,1))
X.genoud <- res$par

## Not run: 
# # Compute actual function and criterion on a grid
# n.grid <- 12 # 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)
# names(design.grid) <- c("V1","V2")
# nt <- nrow(design.grid)
# crit.grid <- apply(design.grid, 1, EQI, model=model, new.noise.var=noise.var, beta=.9)
# 
# # # 2D plots
# z.grid <- matrix(crit.grid, n.grid, n.grid)
# tit <- "Green: best point found by optimizer"
# filled.contour(x.grid,y.grid, z.grid, nlevels=50, color = rainbow,
# plot.axes = {title(tit);points(model@X[,1],model@X[,2],pch=17,col="blue");
# points(X.genoud[1],X.genoud[2],pch=17,col="green");
# axis(1); axis(2)})
# ## End(Not run)