integration_design_optim: Function to build integration points (for the SUR criterion)

Description Usage Arguments Details Value References See Also

View source: R/integration_design_optim.R

Description

Modification of the function integration_design from the package KrigInv-package to be usable for SUR-based optimization. Handles two or three objectives. Available important sampling schemes: none so far.

Usage

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integration_design_optim(
  SURcontrol = NULL,
  d = NULL,
  lower,
  upper,
  model = NULL,
  min.prob = 0.001
)

Arguments

SURcontrol

Optional list specifying the procedure to build the integration points and weights. Many options are possible; see 'Details'.

d

The dimension of the input set. If not provided d is set equal to the length of lower.

lower

Vector containing the lower bounds of the design space.

upper

Vector containing the upper bounds of the design space.

model

A list of kriging models of km class.

min.prob

This argument applies only when importance sampling distributions are chosen. For numerical reasons we give a minimum probability for a point to belong to the importance sample. This avoids probabilities equal to zero and importance sampling weights equal to infinity. In an importance sample of M points, the maximum weight becomes 1/min.prob * 1/M.

Details

The SURcontrol argument is a list with possible entries integration.points, integration.weights, n.points, n.candidates, distrib, init.distrib and init.distrib.spec. It can be used in one of the three following ways:

Value

A list with components:

References

V. Picheny (2014), Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction, Statistics and Computing.

See Also

GParetoptim crit_SUR integration_design


GPareto documentation built on May 31, 2021, 5:09 p.m.