Kriging-Based Optimization for Computer Experiments

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Documentation for package ‘DiceOptim’ version 2.1.1

• DESCRIPTION file.

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DiceOptim-package	Kriging-based optimization methods for computer experiments
AEI	Augmented Expected Improvement
AEI.grad	AEI's Gradient
AKG	Approximate Knowledge Gradient (AKG)
AKG.grad	AKG's Gradient
checkPredict	Prevention of numerical instability for a new observation
critcst_optimizer	Maximization of constrained Expected Improvement criteria
crit_AL	Expected Augmented Lagrangian Improvement
crit_EFI	Expected Feasible Improvement
crit_SUR_cst	Stepwise Uncertainty Reduction criterion
DiceOptim	Kriging-based optimization methods for computer experiments
easyEGO	User-friendly wrapper of the functions 'fastEGO.nsteps' and 'TREGO.nsteps'. Generates initial DOEs and kriging models (objects of class 'km'), and executes 'nsteps' iterations of either EGO or TREGO.
easyEGO.cst	EGO algorithm with constraints
EGO.cst	Sequential constrained Expected Improvement maximization and model re-estimation, with a number of iterations fixed in advance by the user
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
fastEGO.nsteps	Sequential EI maximization and model re-estimation, with a number of iterations fixed in advance by the user
fastfun	Fastfun function
integration_design_cst	Generic function to build integration points (for the SUR criterion)
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_crit	Maximization of the Expected Improvement criterion
max_EI	Maximization of the Expected Improvement criterion
max_EQI	Maximizer of the Expected Quantile Improvement criterion function
max_qEI	Maximization of multipoint expected improvement criterion (qEI)
min_quantile	Minimization of the Kriging quantile.
noisy.optimizer	Optimization of homogenously noisy functions based on Kriging
ParrConstraint	2D constraint function
qEGO.nsteps	Sequential multipoint Expected improvement (qEI) maximizations and model re-estimation
qEI	Analytical expression of the multipoint expected improvement (qEI) criterion
qEI.grad	Gradient of the multipoint expected improvement (qEI) criterion
sampleFromEI	Sampling points according to the expected improvement criterion
test_feas_vec	Test constraints violation (vectorized)
TREGO.nsteps	Trust-region based EGO algorithm.
update_km_noisyEGO	Update of one or two Kriging models when adding new observation