Ken Knoblauch <ken.knoblauch <at> inserm.fr> writes:
>
> 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.
>
and just to follow-up on my own suggestion
library(Rcpp)
cppFunction('
double ss(NumericVector X){
int n = X.size();
double total = 0;
for(int i = 0; i < n; i++)
for(int j = 0; j < n; j++)
total += (X[i] - X[j]) * (X[i] - X[j]);
return total;
}'
)
system.time(ss(X))
user system elapsed
0.002 0.000 0.002
--
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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