minimize | R Documentation |

`minimize`

takes an unconditional score and
a constraint set (or no constraint) and solves the corresponding
minimization problem using
`nloptr`

(using COBYLA by default).
An initial design has to be defined. It is also possible to define
lower- and upper-boundary designs. If this is not done, the boundaries are
determined automatically heuristically.

```
minimize(
objective,
subject_to,
initial_design,
lower_boundary_design = get_lower_boundary_design(initial_design),
upper_boundary_design = get_upper_boundary_design(initial_design),
opts = list(algorithm = "NLOPT_LN_COBYLA", xtol_rel = 1e-05, maxeval = 10000),
...
)
```

`objective` |
objective function |

`subject_to` |
constraint collection |

`initial_design` |
initial guess (x0 for nloptr) |

`lower_boundary_design` |
design specifying the lower boundary. |

`upper_boundary_design` |
design specifying the upper boundary |

`opts` |
options list passed to nloptr |

`...` |
further optional arguments passed to |

a list with elements:

`design` |
The resulting optimal design |

`nloptr_return` |
Output of the corresponding nloptr call |

`call_args` |
The arguments given to the optimization call |

```
# Define Type one error rate
toer <- Power(Normal(), PointMassPrior(0.0, 1))
# Define Power at delta = 0.4
pow <- Power(Normal(), PointMassPrior(0.4, 1))
# Define expected sample size at delta = 0.4
ess <- ExpectedSampleSize(Normal(), PointMassPrior(0.4, 1))
# Compute design minimizing ess subject to power and toer constraints
minimize(
ess,
subject_to(
toer <= 0.025,
pow >= 0.9
),
initial_design = TwoStageDesign(50, .0, 2.0, 60.0, 2.0, 5L)
)
```

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