Kriging-based optimization for computer experiments

Documentation for package ‘DiceOptim’ version 1.0

CL.nsteps	Parallelized version of EGO.nsteps, based on the CL strategy
DiceOptim	Kriging-based optimization methods for computer experiments
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 (noise-free version)
EI.grad	Analytical gradient of the Expected Improvement criterion (noise-free version)
max_EI	One-shot maximization of the Expected Improvement criterion (noise-free version)
max_qEI.CL	One-shot pseudo-maximization of qEI using the Constant Liar strategy
qEI	Monte-Carlo estimation of the multipoints Expected Improvement criterion (noise-free version)