Description Usage Arguments Details Value References See Also

View source: R/integration_design_optim.R

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.

1 2 3 4 5 6 7 8 | ```
integration_design_optim(
SURcontrol = NULL,
d = NULL,
lower,
upper,
model = NULL,
min.prob = 0.001
)
``` |

`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 |

`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 |

`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 |

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:

A) If nothing is specified,

`100 * d`

points are chosen using the Sobol sequence;B) One can directly set the field

`integration.points`

(`p * d`

matrix) for prespecified integration points. In this case these integration points and the corresponding vector`integration.weights`

will be used for all the iterations of the algorithm;C) If the field

`integration.points`

is not set then the integration points are renewed at each iteration. In that case one can control the number of integration points`n.points`

(default:`100*d`

) and a specific distribution`distrib`

. Possible values for distrib are: "`sobol`

", "`MC`

" and "`SUR`

" (default: "`sobol`

"):C.1) The choice "

`sobol`

" corresponds to integration points chosen with the Sobol sequence in dimension`d`

(uniform weight);C.2) The choice "

`MC`

" corresponds to points chosen randomly, uniformly on the domain;C.3) The choice "

`SUR`

" corresponds to importance sampling distributions (unequal weights).

When important sampling procedures are chosen,`n.points`

points are chosen using importance sampling among a discrete set of`n.candidates`

points (default:`n.points*10`

) which are distributed according to a distribution`init.distrib`

(default: "`sobol`

"). Possible values for`init.distrib`

are the space filling distributions "`sobol`

" and "`MC`

" or an user defined distribution "`spec`

". The "`sobol`

" and "`MC`

" choices correspond to quasi random and random points in the domain. If the "`spec`

" value is chosen the user must fill in manually the field`init.distrib.spec`

to specify himself a`n.candidates * d`

matrix of points in dimension`d`

.

A list with components:

`integration.points`

`p x d`

matrix of p points used for the numerical calculation of integrals`integration.weights`

a vector of size`p`

corresponding to the weight of each point. If all the points are equally weighted,`integration.weights`

is set to`NULL`

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

`GParetoptim`

`crit_SUR`

`integration_design`

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.