Expected Quantile Improvement
AEI's Gradient
Approximate Knowledge Gradient (AKG)
Augmented Expected Improvement
AKG's Gradient
Kriging-based optimization methods for computer experiments
Sequential constrained Expected Improvement maximization and model re-estimation,
with a number of iterations fixed in advance by the user
Analytical expression of the Expected Improvement criterion
Sequential EI maximization and model re-estimation, with a number of
iterations fixed in advance by the user
Analytical gradient of the Expected Improvement criterion
EQI's Gradient
Prevention of numerical instability for a new observation
Class for fast to compute objective.
2D constraint function
Fastfun function
Analytical gradient of the Kriging quantile of level beta
Maximizer of the Augmented Expected Improvement criterion function
Maximization of constrained Expected Improvement criteria
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. Generic function to build integration points (for the SUR criterion)
Kriging quantile
Expected Augmented Lagrangian Improvement
2D test function
Update of one or two Kriging models when adding new observation
Maximizer of the Expected Quantile Improvement criterion function
Trust-region based EGO algorithm.
4D test function
EGO algorithm with constraints
Maximization of the Expected Improvement criterion
2D test function
Analytical expression of the multipoint expected improvement (qEI)
criterion
Test constraints violation (vectorized)
Maximizer of the Expected Quantile Improvement criterion function
Maximization of the Expected Improvement criterion
Gradient of the multipoint expected improvement (qEI) criterion
Expected Feasible Improvement
Stepwise Uncertainty Reduction criterion
Optimization of homogenously noisy functions based on Kriging
6D sphere function
Sequential multipoint Expected improvement (qEI) maximizations and model
re-estimation
Sequential EI maximization and model re-estimation, with a number of
iterations fixed in advance by the user
Maximization of multipoint expected improvement criterion (qEI)
Minimization of the Kriging quantile.
4D test function
Sampling points according to the expected improvement criterion