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DiceOptim (version 2.1.2)

Kriging-Based Optimization for Computer Experiments

Description

Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") , parallel infill, and problems with constraints.

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Install

install.packages('DiceOptim')

Monthly Downloads

109

Version

2.1.2

License

GPL-2 | GPL-3

Maintainer

Mickael Binois

Last Published

November 12th, 2025

Functions in DiceOptim (2.1.2)

EGO.nsteps

Sequential EI maximization and model re-estimation, with a number of iterations fixed in advance by the user
AEI.grad

AEI's Gradient
EQI

Expected Quantile Improvement
AKG

Approximate Knowledge Gradient (AKG)
DiceOptim-package

Kriging-based optimization methods for computer experiments
EI

Analytical expression of the Expected Improvement criterion
checkPredict

Prevention of numerical instability for a new observation
crit_EFI

Expected Feasible Improvement
TREGO.nsteps

Trust-region based EGO algorithm.
EQI.grad

EQI's Gradient
ParrConstraint

2D constraint function
crit_SUR_cst

Stepwise Uncertainty Reduction criterion
crit_AL

Expected Augmented Lagrangian Improvement
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.
critcst_optimizer

Maximization of constrained Expected Improvement criteria
branin2

2D test function
max_AEI

Maximizer of the Augmented Expected Improvement criterion function
kriging.quantile

Kriging quantile
hartman4

4D test function
easyEGO.cst

EGO algorithm with constraints
fastfun

Fastfun function
integration_design_cst

Generic function to build integration points (for the SUR criterion)
fastfun-class

Class for fast to compute objective.
fastEGO.nsteps

Sequential EI maximization and model re-estimation, with a number of iterations fixed in advance by the user
goldsteinprice

2D test function
kriging.quantile.grad

Analytical gradient of the Kriging quantile of level beta
qEI

Analytical expression of the multipoint expected improvement (qEI) criterion
max_EQI

Maximizer of the Expected Quantile Improvement criterion function
max_AKG

Maximizer of the Expected Quantile Improvement criterion function
min_quantile

Minimization of the Kriging quantile.
noisy.optimizer

Optimization of homogenously noisy functions based on Kriging
max_EI

Maximization of the Expected Improvement criterion
max_qEI

Maximization of multipoint expected improvement criterion (qEI)
max_crit

Maximization of the Expected Improvement criterion
qEGO.nsteps

Sequential multipoint Expected improvement (qEI) maximizations and model re-estimation
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)
rosenbrock4

4D test function
sphere6

6D sphere function
update_km_noisyEGO

Update of one or two Kriging models when adding new observation
EGO.cst

Sequential constrained Expected Improvement maximization and model re-estimation, with a number of iterations fixed in advance by the user
AKG.grad

AKG's Gradient
AEI

Augmented Expected Improvement
EI.grad

Analytical gradient of the Expected Improvement criterion