Shouldn't such a fallback method be on the books for 0.4? I thought
Suitesparse was handling sparse arrays?
On Sunday, December 21, 2014 12:24:03 PM UTC+1, Tim Holy wrote:
>
> It looks like no one has written the required methods yet. A really
> efficient
> method that exploits the sparseness of b would require some custom code.
> But I
> am surprised that there isn't a generic AbstractArray fallback that works.
> That fallback would be much more efficient in 0.4 than 0.3 (in 0.3 that
> fallback
> would go through linear indexing, which is slow for a sparse matrix). But
> both
> would pale by comparison to an algorithm specifically tuned to exploit
> sparseness.
>
> --Tim
>
> On Saturday, December 20, 2014 08:25:24 PM Robert Gates wrote:
> > Hi Julians,
> >
> > When trying this, I get (Julia 0.3.3):
> >
> > *julia> **b = sub(a,1:2,1:2)*
> >
> > *2x2
> >
> SubArray{Float64,2,SparseMatrixCSC{Float64,Int64},(UnitRange{Int64},UnitRang
>
> > e{Int64})}:*
> >
> > * 1.0 0.0*
> >
> > * 0.0 1.0*
> >
> >
> > *julia> **b*ones(2)*
> >
> > *ERROR: `A_mul_B!` has no method matching A_mul_B!(::Array{Float64,1},
> >
> >
> ::SubArray{Float64,2,SparseMatrixCSC{Float64,Int64},(UnitRange{Int64},UnitRa
>
> > ::nge{Int64})}, Array{Float64,1})*
> >
> > * in * at linalg/matmul.jl:72*
> >
> > Am I doing something wrong? If it's unsupported, does multiplication of
> > sparse subarrays work in 0.4?
> >
> > Best regards,
> >
> > Robert
>
>
