Hi,
Say I have a sparse Matrix X, and a sparse vector (stored as a 1-column
sparse Matrix A), with X and A having the same number of rows, and I
wish to multiply each column of X by A, but would like the operation to
take full advantage of the sparseness of both X and A. In other words I
want the result to be another sparse Matrix but not having any zeros
calculated or stored unnecessarily. For concreteness,
library(Matrix)
set.seed(6860)
X <- sparseMatrix(i = sample(1:10, 5L),
j = sample(1:10, 5L),
x = rep(1, 5),
dims = c(10L, 10L)
)
A <- sparseMatrix(i = sample(1:10, 5L),
j = rep(1L, 5L),
x = rep(1, 5),
dims = c(10L, 1L)
)
and observe that
print(X * A[, 1L, drop=TRUE])
gives the following, in which three 0s are not represented sparsely,
10 x 10 sparse Matrix of class "dgCMatrix"
[1,] . . . . . . . . . .
[2,] . . 1 . . . . . . .
[3,] . . . . . . . . . .
[4,] . . . . . 0 . . . .
[5,] . . . . . . 0 . . .
[6,] . 1 . . . . . . . .
[7,] . . . . . . . . . .
[8,] . . . . . . . . . .
[9,] . . . . . . . . . .
[10,] 0 . . . . . . . . .
in other words I am wondering if there is a more efficient way to arrive
at the same result as,
print(X * A[, rep(1L, ncol(X)), drop=FALSE])
10 x 10 sparse Matrix of class "dgCMatrix"
[1,] . . . . . . . . . .
[2,] . . 1 . . . . . . .
[3,] . . . . . . . . . .
[4,] . . . . . . . . . .
[5,] . . . . . . . . . .
[6,] . 1 . . . . . . . .
[7,] . . . . . . . . . .
[8,] . . . . . . . . . .
[9,] . . . . . . . . . .
[10,] . . . . . . . . . .
without the additional overhead of duplicating A for ncol(X) times.
This seems like such a simple thing, but has me stumped. Any ideas?
Regards,
Ben
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