DiceOptim (version 2.1.1)

easyEGO: User-friendly wrapper of the functions fastEGO.nsteps and TREGO.nsteps. Generates initial DOEs and kriging models (objects of class km), and executes nsteps iterations of either EGO or TREGO.

Description

User-friendly wrapper of the functions fastEGO.nsteps and TREGO.nsteps. Generates initial DOEs and kriging models (objects of class km), and executes nsteps iterations of either EGO or TREGO.

Usage

easyEGO(
  fun,
  budget,
  lower,
  upper,
  X = NULL,
  y = NULL,
  control = list(trace = 1, seed = 42),
  n.cores = 1,
  ...
)

Arguments

fun

scalar function to be minimized,

budget

total number of calls to the objective and constraint functions,

lower

vector of lower bounds for the variables to be optimized over,

upper

vector of upper bounds for the variables to be optimized over,

X

initial design of experiments. If not provided, X is taken as a maximin LHD with budget/3 points

y

initial set of objective observations \(f(X)\). Computed if not provided.

control

an optional list of control parameters. See "Details".

n.cores

number of cores for parallel computation

...

additional parameters to be given to fun

Value

A list with components:

• par: the best feasible point

• values: a vector of the objective and constraints at the point given in par,

• history: a list containing all the points visited by the algorithm (X) and their corresponding objectives (y).

• model: the last GP model, class km

• control: full list of control values, see "Details"

• res: the output of either fastEGO.nsteps or TREGO.nsteps

Details

Does not require knowledge on kriging models (objects of class km) The control argument is a list that can supply any of the following components:

• trace: between -1 and 3

• seed: to fix the seed of the run

• cov.reestim: Boolean, if TRUE (default) the covariance parameters are re-estimated at each iteration

• model.trend: trend for the GP model

• lb, ub: lower and upper bounds for the GP covariance ranges

• nugget: optional nugget effect

• covtype: covariance of the GP model (default "matern5_2")

• optim.method: optimisation of the GP hyperparameters (default "BFGS")

• multistart: number of restarts of BFGS

• gpmean.trick, gpmean.freq: Boolean and integer, resp., for the gpmean trick

• scaling: Boolean, activates input scaling

• warping: Boolean, activates output warping

• TR: Boolean, activates TREGO instead of EGO

• trcontrol: list of parameters of the trust region, see TREGO.nsteps

• always.sample: Boolean, activates force observation even if it leads to poor conditioning

References

D.R. Jones, M. Schonlau, and W.J. Welch (1998), Efficient global optimization of expensive black-box functions, Journal of Global Optimization, 13, 455-492.

Examples

# NOT RUN {
library(parallel)
library(DiceOptim)
set.seed(123)

#########################################################
### 	10 ITERATIONS OF TREGO ON THE BRANIN FUNCTION, ####
###	 STARTING FROM A 9-POINTS FACTORIAL DESIGN       ####
########################################################

# a 9-points factorial design, and the corresponding response
ylim=NULL
fun <- branin; d <- 2
budget <- 5*d
lower <- rep(0,d)
upper <- rep(1,d)
n.init <- 2*d

control <- list(n.init=2*d, TR=TRUE, nugget=1e-5, trcontrol=list(algo="TREGO"), multistart=1)

res1 <- easyEGO(fun=fun, budget=budget, lower=lower, upper=upper, control=control, n.cores=1)

par(mfrow=c(3,1))
y <- res1$history$y
steps <- res1$res$all.steps
success <- res1$res$all.success
sigma <- res1$res$all.sigma
ymin <- cummin(y)
pch <- rep(1, length(sigma))
col <- rep("red", length(sigma))
pch[which(!steps)] <- 2
col[which(success)] <- "darkgreen"

pch2 <- c(rep(3, n.init), pch)
col2 <- c(rep("black", n.init), col)
plot(y, col=col2, ylim=ylim, pch=pch2, lwd=2, xlim=c(0, budget))
lines(ymin, col="darkgreen")
abline(v=n.init+.5)

plot(n.init + (1:length(sigma)), sigma, xlim=c(0, budget), ylim=c(0, max(sigma)), 
pch=pch, col=col, lwd=2, main="TR size") 
lines(n.init + (1:length(sigma)), sigma, xlim=c(0, budget)) 
abline(v=n.init+.5)

plot(NA, xlim=c(0, budget), ylim=c(0, 1), main="x0 (coordinates)")
for (i in 1:d) {
  lines(n.init + (1:nrow(res1$res$all.x0)), res1$res$all.x0[,i]) 
  points(n.init + (1:nrow(res1$res$all.x0)), res1$res$all.x0[,i], pch=pch, col=col, lwd=2) 
}
abline(v=n.init+.5)

par(mfrow=c(1,1))
pairs(res1$model@X, pch=pch2, col=col2)

# }