multiREMBO: Random Embedding Bayesian Optimization

In mbinois/RRembo: Random EMbedding Bayesian Optimization for R

Random Embedding Bayesian Optimization

Description

REMBO for multi-objective unconstrained problems

Usage

multiREMBO(
  par,
  fn,
  lower,
  upper,
  budget,
  ...,
  critcontrol = list(distance = "euclidean", threshold = 1e-04),
  kmcontrol = list(covtype = "matern5_2", covreestim = TRUE, iso = TRUE, formula = ~1),
  control = list(Atype = "isotropic", reverse = TRUE, bxsize = NULL, testU = TRUE,
    standard = FALSE, maxitOptA = 100, lightreturn = FALSE, warping = "Psi", designtype =
    "unif", roll = F, inneroptim = "pso", popsize = 80, gen = 40),
  init = NULL
)

Arguments

par	vector whose length is used to define the initial DoE or just the low dimension
fn	function to be minimized over
lower, upper	bounds for optimization
budget	total number of calls to the objective function
...	additional parameters of fn
critcontrol	optional controls for hypervolume computations, see crit_EHI
kmcontrol	an optional list of control parameters to be passed to the km model: iso, covtype, formula. In addition, boolean codereestim is passed to update.km
control	an optional list of control parameters. See "Details"
init	optional list with elements Amat to provide a random matrix, low_dim_design for an initial design in the low-dimensional space and fvalues for the corresponding response. When passing initial response values, care should be taken that the mapping with Amat of the design actually correspond to high-dimensional designs giving fvalues.

Details

Options available from control are:

• Atype see selectA;

• reverse if TRUE, use the new mapping from the zonotope, otherwise the original mapping with convex projection;

• Amat matrix defining the random embedding;

• bxsize scalar controling the box size in the low-dimensional space;

• testU with the regular mapping, set to TRUE to check that points are in U (to avoid non-injectivity);

• standard for using settings of the original REMBO method;

• maxitOptA if Atype is optimized, number of optimization iterations;

• lightreturn only returns par and value;

• warping either "standard" for kY, "kX" or "Psi";

• designtype one of "LHS", "maximin" and 'unif', see designZ or designU;

• tcheckP minimal distance to an existing solution, see checkPredict

• roll to alternate between optimization methods;

• initdesigns initial design matrix

• inneroptim optimization method for EI

• popsize, gen population size and number of optimization generations of EI

Examples

## Not run: 
set.seed(42)
library(GPareto)
library(eaf)

lowd <- 2
highD <- 25

maxEval <- 100

n.grid <- 101
test.grid <- expand.grid(seq(0, 1, length.out = n.grid), seq(0, 1, length.out = n.grid))
response.grid <- t(apply(test.grid, 1, P1))
PFref <- response.grid[is_nondominated(response.grid),]

ii <- c(1,2)
P1_mod <- function(X){
  if(is.null(nrow(X))) X <- matrix(X, nrow = 1)
  return(P1(X[,c(ii[1], ii[2]), drop = FALSE]))
}

plot(response.grid, pch = '.', xlab = "f1", ylab = "f2")                   
plotParetoEmp(PFref, col = "red")

sol <- multiREMBO(par = rep(NA, lowd), P1_mod, lower = rep(0, highD),
                   upper = rep(1, highD), budget = maxEval)
points(sol$values, col = "blue", pch = 20)


## End(Not run)

mbinois/RRembo documentation built on Sept. 16, 2023, 10:15 p.m.