View source: R/optimInterface.R

optimInterface | R Documentation |

This function is an interface fashioned like the `optim`

function.
Unlike optim, it collects a set of bound-constrained optimization algorithms
with local as well as global approaches. It is, e.g., used in the CEGO package
to solve the optimization problem that occurs during parameter estimation
in the Kriging model (based on Maximum Likelihood Estimation).
Note that this function is NOT applicable to combinatorial optimization problems.

```
optimInterface(x, fun, lower = -Inf, upper = Inf, control = list(), ...)
```

`x` |
is a point (vector) in the decision space of |

`fun` |
is the target function of type |

`lower` |
is a vector that defines the lower boundary of search space |

`upper` |
is a vector that defines the upper boundary of search space |

`control` |
is a list of additional settings. See details. |

`...` |
additional parameters to be passed on to |

The control list contains:

`funEvals`

stopping criterion, number of evaluations allowed for

`fun`

(defaults to 100)`reltol`

stopping criterion, relative tolerance (default: 1e-6)

`factr`

stopping criterion, specifying relative tolerance parameter factr for the L-BFGS-B method in the optim function (default: 1e10)

`popsize`

population size or number of particles (default:

`10*dimension`

, where`dimension`

is derived from the length of the vector`lower`

).`restarts`

whether to perform restarts (Default: TRUE). Restarts will only be performed if some of the evaluation budget is left once the algorithm stopped due to some stopping criterion (e.g., reltol).

`method`

will be used to choose the optimization method from the following list: "L-BFGS-B" - BFGS quasi-Newton:

`stats`

Package`optim`

function

"nlminb" - box-constrained optimization using PORT routines:`stats`

Package`nlminb`

function

"DEoptim" - Differential Evolution implementation:`DEoptim`

Package

Additionally to the above methods, several methods from the package`nloptr`

can be chosen. The complete list of suitable nlopt methods (non-gradient, bound constraints) is:

"NLOPT_GN_DIRECT","NLOPT_GN_DIRECT_L","NLOPT_GN_DIRECT_L_RAND", "NLOPT_GN_DIRECT_NOSCAL","NLOPT_GN_DIRECT_L_NOSCAL","NLOPT_GN_DIRECT_L_RAND_NOSCAL", "NLOPT_GN_ORIG_DIRECT","NLOPT_GN_ORIG_DIRECT_L","NLOPT_LN_PRAXIS", "NLOPT_GN_CRS2_LM","NLOPT_LN_COBYLA", "NLOPT_LN_NELDERMEAD","NLOPT_LN_SBPLX","NLOPT_LN_BOBYQA","NLOPT_GN_ISRES"

All of the above methods use bound constraints. For references and details on the specific methods, please check the documentation of the packages that provide them.

This function returns a list with:

`xbest`

parameters of the found solution

`ybest`

target function value of the found solution

`count`

number of evaluations of

`fun`

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