# nlminb

##### Optimization using PORT routines

Unconstrained and box-constrained optimization using PORT routines.

For historical compatibility.

- Keywords
- optimize

##### Usage

```
nlminb(start, objective, gradient = NULL, hessian = NULL, …,
scale = 1, control = list(), lower = -Inf, upper = Inf)
```

##### Arguments

- start
numeric vector, initial values for the parameters to be optimized.

- objective
Function to be minimized. Must return a scalar value. The first argument to

`objective`

is the vector of parameters to be optimized, whose initial values are supplied through`start`

. Further arguments (fixed during the course of the optimization) to`objective`

may be specified as well (see`…`

).- gradient
Optional function that takes the same arguments as

`objective`

and evaluates the gradient of`objective`

at its first argument. Must return a vector as long as`start`

.- hessian
Optional function that takes the same arguments as

`objective`

and evaluates the hessian of`objective`

at its first argument. Must return a square matrix of order`length(start)`

. Only the lower triangle is used.- …
Further arguments to be supplied to

`objective`

.- scale
See PORT documentation (or leave alone).

- control
A list of control parameters. See below for details.

- lower, upper
vectors of lower and upper bounds, replicated to be as long as

`start`

. If unspecified, all parameters are assumed to be unconstrained.

##### Details

Any names of `start`

are passed on to `objective`

and where
applicable, `gradient`

and `hessian`

. The parameter vector
will be coerced to double.

If any of the functions returns `NA`

or `NaN`

this is an
error for the gradient and Hessian, and such values for function
evaluation are replaced by `+Inf`

with a warning.

##### Value

A list with components:

The best set of parameters found.

The value of `objective`

corresponding to `par`

.

An integer code. `0`

indicates successful
convergence.

A character string giving any additional information returned by the
optimizer, or `NULL`

. For details, see PORT documentation.

Number of iterations performed.

Number of objective function and gradient function evaluations

##### Control parameters

Possible names in the `control`

list and their default values
are:

`eval.max`

Maximum number of evaluations of the objective function allowed. Defaults to 200.

% MXFCAL
`iter.max`

Maximum number of iterations allowed. Defaults to 150.

% MXITER
`trace`

The value of the objective function and the parameters is printed every trace'th iteration. Defaults to 0 which indicates no trace information is to be printed.

`abs.tol`

Absolute tolerance. Defaults to 0 so the absolute convergence test is not used. If the objective function is known to be non-negative, the previous default of

`1e-20`

would be more appropriate. % AFCTOL 31
`rel.tol`

Relative tolerance. Defaults to

`1e-10`

. % RFCTOL 32
`x.tol`

X tolerance. Defaults to

`1.5e-8`

. % XCTOL 33
`xf.tol`

false convergence tolerance. Defaults to

`2.2e-14`

. % XFTOL 34
`step.min, step.max`

Minimum and maximum step size. Both default to

`1.`

. % LMAX0 35 / LMAXS 36
- sing.tol
singular convergence tolerance; defaults to

`rel.tol`

. % SCTOL 37
- scale.init
...

% DINIT 38
- diff.g
an estimated bound on the relative error in the objective function value.

% ETA0 42

##### References

David M. Gay (1990), Usage summary for selected optimization routines. Computing Science Technical Report 153, AT&T Bell Laboratories, Murray Hill.

##### See Also

`optim`

(which is preferred) and `nlm`

.

`optimize`

for one-dimensional minimization and
`constrOptim`

for constrained optimization.

##### Examples

`library(stats)`

```
# NOT RUN {
x <- rnbinom(100, mu = 10, size = 10)
hdev <- function(par)
-sum(dnbinom(x, mu = par[1], size = par[2], log = TRUE))
nlminb(c(9, 12), hdev)
nlminb(c(20, 20), hdev, lower = 0, upper = Inf)
nlminb(c(20, 20), hdev, lower = 0.001, upper = Inf)
## slightly modified from the S-PLUS help page for nlminb
# this example minimizes a sum of squares with known solution y
sumsq <- function( x, y) {sum((x-y)^2)}
y <- rep(1,5)
x0 <- rnorm(length(y))
nlminb(start = x0, sumsq, y = y)
# now use bounds with a y that has some components outside the bounds
y <- c( 0, 2, 0, -2, 0)
nlminb(start = x0, sumsq, lower = -1, upper = 1, y = y)
# try using the gradient
sumsq.g <- function(x, y) 2*(x-y)
nlminb(start = x0, sumsq, sumsq.g,
lower = -1, upper = 1, y = y)
# now use the hessian, too
sumsq.h <- function(x, y) diag(2, nrow = length(x))
nlminb(start = x0, sumsq, sumsq.g, sumsq.h,
lower = -1, upper = 1, y = y)
## Rest lifted from optim help page
fr <- function(x) { ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
grr <- function(x) { ## Gradient of 'fr'
x1 <- x[1]
x2 <- x[2]
c(-400 * x1 * (x2 - x1 * x1) - 2 * (1 - x1),
200 * (x2 - x1 * x1))
}
nlminb(c(-1.2,1), fr)
nlminb(c(-1.2,1), fr, grr)
flb <- function(x)
{ p <- length(x); sum(c(1, rep(4, p-1)) * (x - c(1, x[-p])^2)^2) }
## 25-dimensional box constrained
## par[24] is *not* at boundary
nlminb(rep(3, 25), flb, lower = rep(2, 25), upper = rep(4, 25))
## trying to use a too small tolerance:
r <- nlminb(rep(3, 25), flb, control = list(rel.tol = 1e-16))
stopifnot(grepl("rel.tol", r$message))
# }
```

*Documentation reproduced from package stats, version 3.4.3, License: Part of R 3.4.3*