Global optimization using the Universal Prediction distribution

upego(model, fun, nsteps, lower, upper, seed = 1, parinit = NULL,
  control = NULL, EEIcontrol = NULL)

Arguments

model

the surrogate model

fun

the real function

nsteps

the number of points to be generated

lower

the lower bound of the design space

upper

the upper bound of the design space

seed

the random seed (default = 1)

parinit

inital points to be used in the optimization (default NULL)

control

the optimization control parameters (default NULL)

EEIcontrol

the optimization criterion parameters (default NULL)

Value

list of generated points and their values and the last updated surrogate model

Examples

#' library(UP) d <- 2; n <- 16 X <- expand.grid(x1=s <- seq(0,1, length=5), x2=s) Y <- apply(X, 1, branin) upsm <- UPSM$new(sm= krigingsm$new(), UP=UPClass$new(X,Y,Scale =TRUE) ) upego_res <- upego( upsm,fun = branin, nsteps = 1, lower= c(0,0),upper = c(1,1) )
#> [1] "OPTIMIZATION step : 1 ..." #> [1] "new point " #> x1 x2 #> [1,] 0.125 0.625 #> [1] "New value 8.43883174131785" #> [1] "current best value : 2.94383349064161 inital : 2.94383349064161"
print( min(upego_res$last$get_DOE()$y ) )
#> [1] 2.943833