Kriging-based optimization for computer experiments

Documentation for package ‘DiceOptim’ version 1.4

AEI	Augmented Expected Improvement
AEI.grad	AEI's Gradient
AKG	Approximate Knowledge Gradient (AKG)
AKG.grad	AKG's Gradient
CL.nsteps	Parallelized version of EGO.nsteps, based on the CL strategy
DiceOptim	Kriging-based optimization methods for computer experiments
EGO.nsteps	Sequential EI maximization and model re-estimation, with a number of iterations fixed in advance by the user
EI	Analytical expression of the Expected Improvement criterion
EI.grad	Analytical gradient of the Expected Improvement criterion
EQI	Expected Quantile Improvement
EQI.grad	EQI's Gradient
kriging.quantile	Kriging quantile
kriging.quantile.grad	Analytical gradient of the Kriging quantile of level beta
max_AEI	Maximizer of the Augmented Expected Improvement criterion function
max_AKG	Maximizer of the Expected Quantile Improvement criterion function
max_EI	Maximization of the Expected Improvement criterion
max_EQI	Maximizer of the Expected Quantile Improvement criterion function
max_qEI.CL	One-shot pseudo-maximization of qEI using the Constant Liar strategy
min_quantile	Minimization of the Kriging quantile.
noisy.optimizer	Optimization of homogenously noisy functions based on Kriging
qEI	Monte-Carlo estimation of the multipoints Expected Improvement criterion (noise-free version)
update_km_noisyEGO	Update of one or two Kriging models when adding new observation