nlminb {stats}  R Documentation 
Unconstrained and constrained optimization using PORT routines.
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 (possibly
NA/Inf). 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.

scale 
See PORT documentation (or leave alone) 
control 
a list of control parameters. See below for details. 
lower, upper 
vector of lower and upper bounds, replicated to be as long as
start . If unspecified, all parameters are assumed to be
unconstrained.

... 
further arguments to be supplied to objective 
A list with components:
par 
The best set of parameters found. 
objective 
The value of objective corresponding to par . 
convergence 
An integer code. 0 indicates successful
convergence.

message 
A character string giving any additional information returned by the
optimizer, or NULL . For details, see PORT documentation.

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
iter.max
trace
abs.tol
1e20
.rel.tol
1e10
.x.tol
1.5e8
.step.min
2.2e14
.(of R port) Douglas Bates and Deepayan Sarkar.
http://netlib.belllabs.com/netlib/port/
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, obj = 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, obj = sumsq, lower = 1, upper = 1, y = y) # try using the gradient sumsq.g < function(x,y) 2*(xy) nlminb( start = x0, obj = sumsq, grad = sumsq.g, lo = 1, up = 1, y = y) # now use the hessian, too sumsq.h < function(x,y) diag(2, nrow = length(x)) nlminb(st = x0, obj = sumsq, grad = sumsq.g, hes = sumsq.h, lo = 1, up = 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))