Unconstrained and box-constrained optimization using PORT routines.

For historical compatibility.

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

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.

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

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

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.

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

`optim`

(which is preferred) and `nlm`

.

`optimize`

for one-dimensional minimization and
`constrOptim`

for constrained optimization.

# 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)) # }

Run the code above in your browser using DataCamp Workspace