Kriging-Based Optimization for Computer Experiments


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Documentation for package ‘DiceOptim’ version 2.1.1

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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