designZ | R Documentation |
Select design in Z
designZ(p, pA, bxsize, type = "maximin")
p |
number of points of the design |
pA |
orthogonal projection onto Ran(A) matrix |
bxsize |
bounds of the domain |
type |
design type, one of "LHS", "maximin", "unif" |
Mickael Binois
M. Binois, D. Ginsbourger, O. Roustant (2018), On the choice of the low-dimensional domain for global optimization via random embeddings, arXiv:1704.05318
M. Binois (2015), Uncertainty quantification on Pareto fronts and high-dimensional strategies in Bayesian optimization, with applications in multi-objective automotive design, PhD thesis, Mines Saint-Etienne.
## Example of designs in Z
set.seed(42)
d <- 2; D <- 5
A <- selectA(d, D, type = 'optimized')
size <- sqrt(D) # box size of Z
ntest <- 10000
Z <- size * (2 * matrix(runif(ntest * d), ntest, d) - 1)
inZ <- testZ(Z, t(A))
colors <- rep('black', ntest)
colors[inZ] <- 'green'
plot(Z, col = colors, pch = 20, cex = 0.5)
p <- 20
designs1 <- designZ(p, t(A), size)
designs2 <- designZ(p, t(A), size, type = 'LHS')
points(designs1, col = 'red', pch = 20)
points(designs2, col = 'blue', pch = 20)
legend("topright", legend = c("LHS", "Maximin LHS"), col = c("blue", "red"), pch = c(20,20))
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