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DiceOptim (version 2.0)

Kriging-Based Optimization for Computer Experiments

Description

Efficient Global Optimization (EGO) algorithm and adaptations for parallel infill (multipoint EI), problems with noise, and problems with constraints.

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Version

Install

install.packages('DiceOptim')

Monthly Downloads

126

Version

2.0

License

GPL-2 | GPL-3

Maintainer

V. Picheny

Last Published

September 15th, 2016

Functions in DiceOptim (2.0)

AEI.grad

AEI's Gradient
AKG.grad

AKG's Gradient
AKG

Approximate Knowledge Gradient (AKG)
crit_SUR_cst

Stepwise Uncertainty Reduction criterion
critcst_optimizer

Maximization of constrained Expected Improvement criteria
AEI

Augmented Expected Improvement
checkPredict

Prevention of numerical instability for a new observation
crit_AL

Expected Augmented Lagrangian Improvement
branin2

2D test function
crit_EFI

Expected Feasible Improvement
EI.grad

Analytical gradient of the Expected Improvement criterion
EQI.grad

EQI's Gradient
easyEGO.cst

EGO algorithm with constraints
DiceOptim-package

Kriging-based optimization methods for computer experiments
EQI

Expected Quantile Improvement
hartman4

4D test function
fastfun

Fastfun function
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
goldsteinprice

2D test function
max_AEI

Maximizer of the Augmented Expected Improvement criterion function
max_EQI

Maximizer of the Expected Quantile Improvement criterion function
max_qEI

Maximization of multipoint expected improvement criterion (qEI)
kriging.quantile

Kriging quantile
noisy.optimizer

Optimization of homogenously noisy functions based on Kriging
kriging.quantile.grad

Analytical gradient of the Kriging quantile of level beta
min_quantile

Minimization of the Kriging quantile.
integration_design_cst

Generic function to build integration points (for the SUR criterion)
max_AKG

Maximizer of the Expected Quantile Improvement criterion function
max_EI

Maximization of the Expected Improvement criterion
test_feas_vec

Test constraints violation (vectorized)
sampleFromEI

Sampling points according to the expected improvement criterion
update_km_noisyEGO

Update of one or two Kriging models when adding new observation
rosenbrock4

4D test function
qEI.grad

Gradient of the multipoint expected improvement (qEI) criterion
ParrConstraint

2D constraint function
sphere6

6D sphere function
qEGO.nsteps

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