This thread is old, but I was poking through some unread bits and found it.
Beyond that, it’s likely a better question for julia-users list (which I’m
including here).
Anyhow, the answer is that sparsevec converts a dense matrix into a sparse
matrix format where zero values are not explicitly stored. If the vector
(or matrix) really is dense, this does not save space.
julia> A = [23.1, 42.67, 8.246, 111.33]
4-element Array{Float64,1}:
23.1
42.67
8.246
111.33
julia> sparsevec(A)
Sparse vector of length 4 with 4 Float64 nonzero entries:
[1] = 23.1
[2] = 42.67
[3] = 8.246
[4] = 111.33
julia> B = [0,0,A...]
6-element Array{Float64,1}:
0.0
0.0
23.1
42.67
8.246
111.33
julia> sparsevec(B)
Sparse vector of length 6 with 4 Float64 nonzero entries:
[3] = 23.1
[4] = 42.67
[5] = 8.246
[6] = 111.33
Cameron
On Sat, Aug 20, 2016 at 7:15 AM, Parkway <dineshbvad...@hotmail.com> wrote:
> The doc for the function sparsevec(A) says:
> sparsevec(A)
> Convert a dense vector A into a sparse matrix of size m x 1. In julia,
> sparse vectors are really just sparse matrices with one column.
>
> What does this actually mean? For example, what does the dense vector A =
> [23.1, 42.67, 8.246, 111.33] get converted to?
>
>
