ROI (version 0.2-1)

nlminb2: Nonlinear programming with nonlinear constraints.

Description

This function was contributed by Diethelm Wuertz.

Usage

nlminb2(start, objective, eqFun = NULL, leqFun = NULL, lower = -Inf, upper = Inf, gradient = NULL, hessian = NULL, control = list())

Arguments

start

numeric vector of start values.

objective

the function to be minimized $f(x)$.

eqFun

functions specifying equal constraints of the form $h_i(x) = 0$. Default: NULL (no equal constraints).

leqFun

functions specifying less equal constraints of the form $g_i(x) <= 0$.="" default:="" NULL (no less equal constraints).

lower

a numeric representing lower variable bounds. Repeated as needed. Default: -Inf.

upper

a numeric representing upper variable bounds. Repeated as needed. Default: Inf.

gradient

gradient of $f(x)$. Default: NULL (no gradiant information).

hessian

hessian of $f(x)$. Default: NULL (no hessian provided).

control

a list of control parameters. See nlminb() for details. The parameter "scale" is set here in contrast to nlminb() .

Value

list()

Examples

## Equal constraint function
eval_g0_eq <- function( x, params = c(1,1,-1)) {
       return( params[1]*x^2 + params[2]*x + params[3] )
   }
eval_f0 <- function( x, ... ) {
       return( 1 )
   }