n1qn1 (version 6.0.1-3)

qnbd: qnbd optimization

Description

This is an R port of the qnbd which is a BFGS-B optimization procedure in scilab. (R has L-BFGS-B).

Usage

qnbd(par, fn, gr, lower = -Inf, upper = Inf,
  environment = parent.frame(1), zero = sqrt(.Machine$double.eps/7e-07),
  maxFn = 10000L, maxIt = 10000L, epsf = sqrt(.Machine$double.eps),
  epsg = sqrt(.Machine$double.eps), epsx = sqrt(.Machine$double.eps),
  print.functions = FALSE)

Arguments

par

Initial parameter estimate

fn

Function

gr

Gradient

lower

Lower Bound for optimization

upper

Upper Bound for optimization

environment

Environment where call_eval/call_grad are evaluated.

zero

Tolerance for Zero

maxFn

Maximum function evaluations

maxIt

Maximum iterations

epsf

Function eps for exiting

epsg

Gradient eps for exiting

epsx

Parameter eps for exiting

print.functions

Boolean to control if the function value and parameter estimates are echoed every time a function is called.

Examples

# NOT RUN {
## Rosenbrock's banana function
n=3; p=100

fr = function(x)
{
    f=1.0
    for(i in 2:n) {
        f=f+p*(x[i]-x[i-1]**2)**2+(1.0-x[i])**2
    }
    f
}

grr = function(x)
{
    g = double(n)
    g[1]=-4.0*p*(x[2]-x[1]**2)*x[1]
    if(n>2) {
        for(i in 2:(n-1)) {
            g[i]=2.0*p*(x[i]-x[i-1]**2)-4.0*p*(x[i+1]-x[i]**2)*x[i]-2.0*(1.0-x[i])
        }
    }
    g[n]=2.0*p*(x[n]-x[n-1]**2)-2.0*(1.0-x[n])
    g
}

x = c(1.02,1.02,1.02)

op1 <- qnbd(x, fr, grr)

# }