On 25 March 2014 at 11:42, Søren Højsgaard wrote:
| Dear all,
| I have a large sparse adjacency matrix X for an undirected graph. For a
subset 'idx' of the vertices I want to find out if the subgraph defined by this
subset is complete (i.e. has edges between all variables). So in R, one would
check if X[idx,idx] has only ones outside the diagonal, but this is slow and I
want to do it with Rcpp. Here is one attempt where I simply add up the elements
above (or below) the diagonal, and to access the elements of X I use coeff
which is relatively slow (because X is a sparse matrix).
|
| #include <RcppEigen.h>
| typedef Eigen::MappedSparseMatrix<double> MSpMat;
| typedef Eigen::SparseVector<double> SpVec;
| typedef SpVec::InnerIterator InIter;
|
| //[[Rcpp::export]]
| double foo (MSpMat X, Eigen::VectorXi idx){
| SpVec sidx = idx.sparseView();
| double out=0;
| int i2, j2;
| for (InIter i_(sidx); i_; ++i_){
| i2 = i_.index();
| for (InIter j_(sidx); j_; ++j_){
| j2 = j_.index();
| if (i2>j2)
| out += X.coeff( i2, j2);
| }
| }
| return out;
| }
|
| /*** R
| library(Matrix)
| M1 <- new("dgCMatrix"
| , i = c(1L, 2L, 3L, 0L, 2L, 3L, 0L, 1L, 3L, 0L, 1L, 2L, 4L, 5L, 3L, 5L,
3L, 4L)
| , p = c(0L, 3L, 6L, 9L, 14L, 16L, 18L)
| , Dim = c(6L, 6L)
| , Dimnames = list(c("a", "b", "c", "d", "e", "f"), c("a", "b", "c", "d",
"e", "f"))
| , x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
| , factors = list()
| )
| M1
|
| foo(M1, c(2,3,4))
|
| */
| Can anyone see a better way of doing this?
Not really from the top of my head. You do need to access all those elements,
and the traversal in C++ ought to / will be faster than doing it in R.
| I was thinking about whether the sparse matrix iterators could be a solution,
but I can't a hold on it. I was also thinking about using sparse matrices in
RcppArmadillo, but I guess the problem (speed) is the same (haven't tried!).
Sparse matrices are complicated. I heard from Conrad and Ryan (who, over at
mlpack.org is driving some sparse matrix adoption for Armadillo) that they
put in a bunch of work to get a sparse solver going (via an external library)
only to find out that for common problem sizes it was slower than the dense
solver :-/
So while Rcpp may give you some tools, you may still have to do some
algorithmic work.
Dirk
| Any advice will be greatly appreciated.
| Cheers
| Søren
|
|
|
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--
Dirk Eddelbuettel | e...@debian.org | http://dirk.eddelbuettel.com
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