Here is another approach using an augmented penalty lagragian method.
The same example:
fr <- function(x) { ## Rosenbrock Banana function
x1 <- x[1]
x2 <- x[2]
100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
fr.pen <- function(x, k=1e05) {
fr(x) + k * ( 1 - sum(x))^2
}
require(BB)
spg(par=c(0, 0), fn=fr.pen) # Note: this starting value will fail for the
"projection method" due to division by zero
Ravi.
----------------------------------------------------------------------------
-------
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: [email protected]
Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
----------------------------------------------------------------------------
--------
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of dre968
Sent: Wednesday, April 22, 2009 8:23 AM
To: [email protected]
Subject: [R] Help using spg optimization in BB package
i'm trying to use the BB package to minimize the sum of the squared
deviations for 2 vectors. The only thing am having trouble with is defining
the project constraint. I got the upper and lower bounds to work but i am
not sure how to create a constraint that the sum of x must be 1. Any help
would be greatly appreciated.
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