# tnbc: Truncated Newton function minimization with bounds... In optimx: Expanded Replacement and Extension of the 'optim' Function

 tnbc R Documentation

## 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

```   tnbc(x, fgfun, lower, upper, trace=0, ...)
```

### Arguments

 `x` A numeric vector of starting estimates. `fgfun` A function that returns the value of the objective at the supplied set of parameters `par` using auxiliary data in .... The gradient is returned as attribute "gradient". The first argument of `fgfun` must be `par`. `lower` A vector of lower bounds on the parameters. `upper` A vector of upper bounds on the parameters. `trace` Set >0 to cause intermediate output to allow progress to be followed. `...` Further arguments to be passed to `fn`.

### 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. `0` indicates that the method believes it has succeeded; `2` that more than `maxfun` (default 150*n, where there are n parameters); `3` if the line search appears to have failed (which may not be serious); and `-1` if there appears to be an error in the input parameters. `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

`optim`
```## See tn.Rd