R help - Sep 2008 - optimizing speed of calculation (recursive product)

Dear list,
I'm wondering how to optimize functions/processes like the one shown 
below (which simplifies something we're trying to do with very large 
datasets).
In many cases I've noticed that using apply, sapply etc can help 
speeding up processes, but in this case I don't see how I could do so.

a <- runif(10000000,0.5,1.6)
C <- 2
M <- 10000000
system.time( for (i in 1:(M-1)) {C <- C* c(a[i],a[i+1])} )


By the way, I'm using R-2.7.2 on Win XP and/or Fedora Linux.

Thank's in advance for any hints,
Wolfgang Raffelsberger

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Wolfgang Raffelsberger, PhD
Laboratoire de BioInformatique et G?nomique Int?gratives
CNRS UMR7104, IGBMC 
1 rue Laurent Fries,  67404 Illkirch  Strasbourg,  France

Wolfgang Raffelsberger wrote:
> Dear list,
> I'm wondering how to optimize functions/processes like the one shown 
> below (which simplifies something we're trying to do with very large 
> datasets).
> In many cases I've noticed that using apply, sapply etc can help 
> speeding up processes, but in this case I don't see how I could do so.
> 
> a <- runif(10000000,0.5,1.6)
> C <- 2
> M <- 10000000
> system.time( for (i in 1:(M-1)) {C <- C* c(a[i],a[i+1])} )

Well, in principal you could say:

a <- runif(10000000,0.5,1.6)
C <- 2
M <- 10000000
system.time(C <- C * c(prod(a[-M]), prod(a[-1])))

but this is all completely inaccurate from a numerical point of view - 
and you probably end up with c(0, 0) which is the quickest way to 
calculate those numbers in similar accurate way.

Uwe Ligges

> 
> By the way, I'm using R-2.7.2 on Win XP and/or Fedora Linux.
> 
> Thank's in advance for any hints,
> Wolfgang Raffelsberger
> 
> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
> Wolfgang Raffelsberger, PhD
> Laboratoire de BioInformatique et G?nomique Int?gratives
> CNRS UMR7104, IGBMC 1 rue Laurent Fries,  67404 Illkirch  Strasbourg,  
> France
> 
> ______________________________________________
> R-help at r-project.org 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.

Hi

r-help-bounces at r-project.org napsal dne 03.09.2008 16:39:07:
> Dear list,
> I'm wondering how to optimize functions/processes like the one shown 
> below (which simplifies something we're trying to do with very large 
> datasets).
> In many cases I've noticed that using apply, sapply etc can help 
> speeding up processes, but in this case I don't see how I could do so.
> 
> a <- runif(10000000,0.5,1.6)
> C <- 2
> M <- 10000000
> system.time( for (i in 1:(M-1)) {C <- C* c(a[i],a[i+1])} )
Maybe simple math? You want last two members of 2*cumprod(a).

So
> system.time(2*cumprod(a)[9999999:10000000])
   user  system elapsed 
   1.97    0.04    2.00 
>
shall be a little bit quicker then for cycle. But it is valid only for the 
above calculation.

Regards
Petr


> 
> 
> By the way, I'm using R-2.7.2 on Win XP and/or Fedora Linux.
> 
> Thank's in advance for any hints,
> Wolfgang Raffelsberger
> 
> . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
> Wolfgang Raffelsberger, PhD
> Laboratoire de BioInformatique et G?nomique Int?gratives
> CNRS UMR7104, IGBMC 
> 1 rue Laurent Fries,  67404 Illkirch  Strasbourg,  France
> 
> ______________________________________________
> R-help at r-project.org 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.