nlminb {stats}  R Documentation 
Unconstrained and boxconstrained 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 
gradient 
Optional function that takes the same arguments as 
hessian 
Optional function that takes the same arguments as 
... 
Further arguments to be supplied to 
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

Any names of start
are passed on to objective
and where
applicable, gradient
and hessian
. The parameter vector
will be coerced to double.
The PORT documentation is at http://netlib.belllabs.com/cm/cs/cstr/153.pdf.
The parameter vector passed to objective
, gradient
and
hessian
has special semantics and is shared between calls. The
functions should not copy it.
If any of the functions returns NA
or NaN
the internal code
could infiniteloop in R prior to 2.15.2: this is now an error for
the gradient and Hessian, and such values for function evaluation are
replaced by +Inf
with a warning.
A list with components:
par 
The best set of parameters found. 
objective 
The value of 
convergence 
An integer code. 
message 
A character string giving any additional information returned by the
optimizer, or 
iterations 
Number of iterations performed. 
evaluations 
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.
iter.max
Maximum number of iterations allowed. Defaults to 150.
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 nonnegative, the previous default of
1e20
would be more appropriate.
rel.tol
Relative tolerance. Defaults to
1e10
.
x.tol
X tolerance. Defaults to 1.5e8
.
xf.tol
false convergence tolerance. Defaults to
2.2e14
.
step.min, step.max
Minimum and maximum step size. Both
default to 1.
.
singular convergence tolerance; defaults to
rel.tol
.
...
an estimated bound on the relative error in the objective function value.
R port: Douglas Bates and Deepayan Sarkar.
Underlying Fortran code by David M. Gay
http://netlib.belllabs.com/netlib/port/
optim
(which is preferred) and nlm
.
optimize
for onedimensional minimization and
constrOptim
for constrained optimization.
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 SPLUS help page for nlminb # this example minimizes a sum of squares with known solution y sumsq < function( x, y) {sum((xy)^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*(xy) 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, p1)) * (x  c(1, x[p])^2)^2) } ## 25dimensional 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 = 1e16)) stopifnot(grepl("rel.tol", r$message))