vorob_optim_parallel2_gpc {ARCHISSUR}R Documentation

Parallel Vorob'ev criterion

Description

Evaluation of the Vorob'ev criterion for candidate points x, assuming that some other points are also going to be evaluated. To be used in optimization routines, like in max_vorob_parallel_gpc. To avoid numerical instabilities, the new points are evaluated only if they are not too close to an existing observation, or if there is some observation noise. The criterion is the integral of the posterior Vorob'ev uncertainty.

Usage

vorob_optim_parallel2_gpc(
  x,
  other.points,
  integration.points,
  integration.weights = NULL,
  intpoints.oldmean,
  intpoints.oldsd,
  precalc.data,
  object,
  new.noise.var = NULL,
  batchsize,
  alpha,
  current.vorob,
  seed = NULL
)

Arguments

x

input vector of size d at which one wants to evaluate the criterion.

other.points

vector giving the other batchsize-1 points at which one wants to evaluate the criterion.

integration.points

p*d matrix of points for numerical integration in the design space.

integration.weights

vector of size p corresponding to the weights of these integration points.

intpoints.oldmean

vector of size p corresponding to the latent GP mean at the integration points before adding x to the design of experiments.

intpoints.oldsd

vector of size p corresponding to the latent GP standard deviation at the integration points before adding x to the design of experiments.

precalc.data

list containing precalculated data. This list can be generated using the precomputeUpdateData function.

object

object of class gpcm.

new.noise.var

optional scalar value of the noise variance of the new observations.

batchsize

number of points to sample simultaneously. The sampling criterion will return batchsize points at a time for sampling.

alpha

a scalar representing the Vorob'ev threshold.

current.vorob

current value of the vorob criterion (before adding new observations).

seed

to fix the seed.

Value

Parallel Vorob'ev value

Author(s)

Morgane MENZ, Delphine SINOQUET, Miguel MUNOZ-ZUNIGA. Contributors: Naoual SERRAJI.

References

Menz, M., Munoz-Zuniga, M., Sinoquet, D. Estimation of simulation failure set with active learning based on Gaussian Process classifiers and random set theory (2023). https://hal.science/hal-03848238.

Chevalier, C. Fast uncertainty reduction strategies relying on Gaussian process models PhD Thesis. University of Bern (2013).

Bachoc, F., Helbert, C. & Picheny, V. Gaussian process optimization with failures: classification and convergence proof. J Glob Optim 78, 483–506 (2020). doi:10.1007/s10898-020-00920-0.

See Also

max_vorob_parallel_gpc()


[Package ARCHISSUR version 0.0.1 Index]