How about:
> max(apply(A*t(B),1,sum))
or better yet (much faster than apply):
> max(row.sum(A*t(B)))
where:
> row.sum
function(M)
{
nc <- ncol(M)
c(M %*% array(1, c(nc, 1)))
}
The latter resulted in the following times on
Version 3.3 Release 1 for Sun SPARC, SunOS 4.1.x
for 500*10 and 10*500 matrices (about a 20-25 fold improvement
using row.sum):
> unix.time(max(diag(A%*%B)))
[1] 1.833334 0.416667 3.000000 0.000000 0.000000
> unix.time(max(apply(A*t(B),1,sum)))
[1] 0.28333282 0.05000019 1.00000000 0.00000000 0.00000000
> unix.time(max(row.sum(A*t(B))))
[1] 0.08333397 0.01666689 0.00000000 0.00000000 0.00000000
Hope this helps
Franz
***************************************************************
Since all models are wrong, the scientist must be alert
to what is importantly wrong. It is inappropriate to be
concerned about mice when there are tigers abroad.
- Box, 1976
***************************************************************
Franz-Josef Mueter | Voice: (907) 474-7839
Institute of Marine Sciences | (907) 479-8815
School of Fisheries and Ocean Sciences | Fax: (907) 474-1943
University of Alaska | e-mail:
Fairbanks, Alaska, 99709-1080 | mueter@ims.alaska.edu
---------------------------------------------------------------
On Wed, 29 Jul 1998, Marcel Wolbers wrote:
> Dear S-users
>
> I have a n*m matrix A and a m*n matrix B where n is large (say 10000).
> The measure I'm interested in is
> > max(diag(A%*%B))
> However the calculation above takes too long, has to calculate many things
> I don't need (the off-diagonal elements) and uses too much memory for
> n large.
>
> Of course I could avoid the matrix multiplication by using a for-loop, i.e.
> > x <- vector("numeric",10000)
> > for (index in 1:10000)
> > { x[index] <- A[index,]%*%B[,index] }
> > max(x)
>
> But there must be a more efficient way to do it...
> It should be easy but I just can't find it.
>
> Thanks a lot for your help.
>
> Marcel Wolbers
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