Bos, Roger <roger.bos <at> rothschild.com> writes:
> I am using a sum of squared differences in the
objective function of an optimization problem I am
doing and I
> have managed to speed it up using the
outer function versus the nested for loops, but my
suspicion is that
> the calculation could be done even quicker.
Please see the code below for a simple example. If
anyone can
> point out a faster way I would appreciate it greatly.
>
> Thanks,
>
> Roger
>
> X <- runif(1000)
>
> now <- proc.time()
> ans <- 0
> for (i in 1:length(X)) {
> for (j in 1:length(X)) {
> ans <- ans + (X[i]-X[j])*(X[i]-X[j])
> }
> }
> ans
> speed <- proc.time() - now; cat(" That took ",
round(speed[3],1), " secs.\n", sep="")
>
> now <- proc.time()
> gg <- outer(X, X, FUN="-")
> sum(gg*gg)
> speed <- proc.time() - now; cat(" That took ",
round(speed[3],1), " secs.\n", sep="")
>
>
system.time(
for (i in 1:length(X)) {
for (j in 1:length(X)) {
ans <- ans + (X[i]-X[j])*(X[i]-X[j])
}
})
user system elapsed
2.241 0.009 2.293
system.time(2 * sum(c(dist(X))^2))
user system elapsed
0.038 0.002 0.040
and then there is Rcpp if you want to add
some extra grease.
--
Kenneth Knoblauch
Inserm U846
Stem-cell and Brain Research Institute
Department of Integrative Neurosciences
18 avenue du Doyen Lépine
69500 Bron
France
tel: +33 (0)4 72 91 34 77
fax: +33 (0)4 72 91 34 61
portable: +33 (0)6 84 10 64 10
http://www.sbri.fr/members/kenneth-knoblauch.html
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