max_AEI: Maximizer of the Augmented Expected Improvement criterion...

In DiceOptim: Kriging-Based Optimization for Computer Experiments

Description

Maximization, based on the package rgenoud of the Augmented Expected Improvement (AEI) criterion.

Usage

max_AEI(
  model,
  new.noise.var = 0,
  y.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 the new observation.
y.min	The kriging mean prediction at the current best point (point with smallest kriging quantile). If not provided, this quantity is evaluated inside the AEI 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:

par	the best set of parameters found.
value	the value AEI at par.

Author(s)

Victor Picheny

David Ginsbourger

Examples

library(DiceDesign)
set.seed(100)

# 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(lhsDesign(doe.size, dim)$design)
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_AEI
res <- max_AEI(model, new.noise.var=noise.var, type = "UK", 
lower=c(0,0), upper=c(1,1)) 
X.genoud <- res$par

# 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, AEI, model=model, new.noise.var=noise.var)

## Not run: 
# # 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)

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