[R] A faster way to compute finite-difference gradient of a scalar function of a large number of variables

From: Ravi Varadhan <rvaradhan_at_jhmi.edu>
Date: Thu, 27 Mar 2008 11:59:51 -0400

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




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 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: rvaradhan_at_jhmi.edu

Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html  


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