RRembo-package: Package RRembo

In mbinois/RRembo: Random EMbedding Bayesian Optimization for R

Package RRembo

Description

Implementation of the Random EMbedding Bayesian Optimization method with several improvements. Those include domain selection as well as warped kernels.

Details

Important functions:

Author(s)

Mickael Binois

References

Z. Wang, F. Hutter, M. Zoghi, D. Matheson, N. de Freitas (2016), Bayesian Optimization in a Billion Dimensions via Random Embeddings, JAIR.

M. Binois, D. Ginsbourger, O. Roustant (2015), A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings, Learning and Intelligent Optimization, Springer

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.

Examples

## Not run: 
set.seed(42)
library(DiceKriging)

lowd <- 2
highD <- 25

maxEval <- 100

ii <- sample(1:highD, 2)
sol <- easyREMBO(par = rep(NA, lowd), branin_mod, ii = ii, lower = rep(0, highD),
                   upper = rep(1, highD), budget = maxEval)
cat(sol$value, "\n")

## End(Not run)

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