# Truncated Newton function minimization with bounds constraints

### Description

A bounds-constarined R implementation of a truncated Newton method for minimization of nonlinear functions subject to bounds (box) constraints.

### Usage

1 |

### Arguments

`x` |
A numeric vector of starting estimates. |

`fgfun` |
A function that returns the value of the objective at
the supplied set of parameters |

`lower` |
A vector of lower bounds on the parameters. |

`upper` |
A vector of upper bounds on the parameters. |

`trace` |
Set TRUE to cause intermediate output to allow progress to be followed. |

`...` |
Further arguments to be passed to |

### Details

Function `fgfun`

must return a numeric value in list item `f`

and a numeric vector in list item `g`

.

### Value

A list with components:

`xstar` |
The best set of parameters found. |

`f` |
The value of the objective at the best set of parameters found. |

`g` |
The gradient of the objective at the best set of parameters found. |

`ierror` |
An integer indicating the situation on termination. |

`nfngr` |
A number giving a measure of how many conjugate gradient solutions were used during the minimization process. |

### References

Stephen G. Nash (1984) "Newton-type minimization via the Lanczos method", SIAM J Numerical Analysis, vol. 21, no. 4, pages 770-788.

For Matlab code, see http://www.netlib.org/opt/tn

### See Also

`optim`

### Examples

1 | ```
## See tn.Rd
``` |