Thanks everyone,
That's a 3000 fold speedup. Now if only I can get the same improvement on
the stMincuts iGraph algorithm.
On Fri, Apr 25, 2014 at 4:20 AM, Martin Maechler <maech...@stat.math.ethz.ch
> wrote:
> >>>>> Stefan Evert <stefa...@collocations.de>
> >>>>> on Fri, 25 Apr 2014 09:09:31 +0200 writes:
>
> > On 24 Apr 2014, at 23:56, Greg Snow <538...@gmail.com> wrote:
>
> >> library(Matrix)
> >>
> >> adjM <- Matrix(0,nrow=10,ncol=10)
> >>
> >> locs <- cbind( sample(1:10), sample(1:10) )
> >> vals <- rnorm(10)
> >>
> >> adjM[ locs ] <- vals
>
> > ... and once you've got your data in this format, why not construct
> the sparse matrix directly?
>
> > adjM <- sparseMatrix(i = locs[,1], j = locs[,2], x = vals)
>
> > I've found this to be very efficient and have used it with sparse
> matrices containing up to around 100 million nonzero entries.
>
> Yes, indeed, thank you Stefan!
>
> Let me reiterate :
>
> __ Unless you can use special constructors such as
> __
> __ Diagonal() # diagonal matrices
> __ bdiag() # block diagonal matrices
> __ bandSparse() # "banded diagonal" matrices
> __ kronecker(a, b) # where a or b are sparse
> __
> __ (and maybe another one I forgot)
> __
> __ *the* way to efficiently construct large sparse matrices is
> __ sparseMatrix()
> __ or sometimes its alternative / precursor spMatrix().
> __
> __ Matrix(d, ...., sparse=TRUE)
> __
> __ is nice and fine only for relatively *small* matrices,
> __ as it really works from a dense original 'd' (directly or via
> replication)
>
>
> > Hope this helps,
>
> I do hope, too.
> I'm very happy for suggestions on how we as Matrix authors
> could make this better known.
>
> Recently, someone proposed to make the 'rsparseMatrix()'
> utility function from help(sparseMatrix)
> into an "official" Matrix package function. If I did that,
> I could start using rsparseMatrix() in typical examples rather
> than the current often use of Matrix() or
> as(<traditional matrix>, "sparseMatrix")
> both of which are perfect for the small examples that are
> typical for help files.
>
> Martin Maechler,
> ETH Zurich
>
>
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