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d2
2005 Dec 04
1
Understanding nonlinear optimization and Rosenbrock's banana valley function?
...t and hessian
#
# Initial evaluation
f.i <- f(p)
f0 <- f.i+1
# Iterate
for(i in 1:iterlim){
df <- attr(f.i, "gradient")
# Gradient sufficiently small?
if(sum(df^2)<(gradtol^2)){
return(list(minimum=f.i, estimate=p+dp,
gradient=df, hessian=d2f, code=1,
iterations=i))
}
#
d2f <- attr(f.i, "hessian")
dp <- (-solve(d2f, df))
# Step sufficiently small?
if(sum(dp^2)<(steptol^2)){
return(list(minimum=f.i, estimate=p+dp,
gradient=df, hessian=d2f, code=2,
iterations...