Kriging-Based Optimization for Computer Experiments
Sequential constrained Expected Improvement maximization and model re-...
Sequential EI maximization and model re-estimation, with a number of i...
Analytical gradient of the Expected Improvement criterion
Analytical expression of the Expected Improvement criterion
2D test function
4D test function
Generic function to build integration points (for the SUR criterion)
Analytical gradient of the Kriging quantile of level beta
4D test function
User-friendly wrapper of the functions fastEGO.nsteps and `TREGO.nst...
Sequential multipoint Expected improvement (qEI) maximizations and mod...
Gradient of the multipoint expected improvement (qEI) criterion
Analytical expression of the multipoint expected improvement (qEI) cri...
Augmented Expected Improvement
Kriging-based optimization methods for computer experiments
EGO algorithm with constraints
AEI's Gradient
AKG's Gradient
Approximate Knowledge Gradient (AKG)
2D test function
Prevention of numerical instability for a new observation
Expected Augmented Lagrangian Improvement
Expected Feasible Improvement
Stepwise Uncertainty Reduction criterion
Maximization of constrained Expected Improvement criteria
EQI's Gradient
Expected Quantile Improvement
Sequential EI maximization and model re-estimation, with a number of i...
Class for fast to compute objective.
Fastfun function
2D constraint function
Kriging quantile
Maximizer of the Augmented Expected Improvement criterion function
Maximizer of the Expected Quantile Improvement criterion function
Maximization of the Expected Improvement criterion
Maximization of the Expected Improvement criterion
Maximizer of the Expected Quantile Improvement criterion function
Maximization of multipoint expected improvement criterion (qEI)
Minimization of the Kriging quantile.
Optimization of homogenously noisy functions based on Kriging
Sampling points according to the expected improvement criterion
6D sphere function
Test constraints violation (vectorized)
Trust-region based EGO algorithm.
Update of one or two Kriging models when adding new observation
Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" <doi:10.18637/jss.v051.i01> and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") <doi:10.1016/j.csda.2013.03.018>, parallel infill, and problems with constraints.