Hi All,
I would like to compute the simple finite-difference approximation to the
gradient of a scalar function of a large number of variables (on the order
of 1000). Although a one-time computation using the following function
grad() is fast and simple enough, the overhead for repeated evaluation of
gradient in iterative schemes is quite significant. I was wondering whether
there are better, more efficient ways to approximate the gradient of a large
scalar function in R.
Here is an example.
grad <- function(x, fn=func, eps=1.e-07, ...){
npar <- length(x)
df <- rep(NA, npar)
f <- fn(x, ...)
for (i in 1:npar) {
dx <- x
dx[i] <- dx[i] + eps
df[i] <- (fn(dx, ...) - f)/eps
}
df
}
myfunc <- function(x){
nvec <- 1: length(x)
sum(nvec * (exp(x) - x)) / 10
}
myfunc.g <- function(x){
nvec <- 1: length(x)
nvec * (exp(x) - 1) / 10
}
p0 <- rexp(1000)
system.time(g.1 <- grad(x=p0, fn=myfunc))[1]
system.time(g.2 <- myfunc.g(x=p0))[1]
max(abs(g.2 - g.1))
Thanks in advance for any help or hints.
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
----------------------------------------------------------------------------
--------
[[alternative HTML version deleted]]
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
