Add Support for Matrices with Int32 and UInt32 as Indices Vectors

[RoyiAvital]

It seems the code fails when the input sparse matrix has indices with types which are not Int64:

using SparseArrays;
using LimitedLDLFactorizations;

mA = sprand(10, 10, 0.1);
vI, vJ, vK = findnz(mA);
mB = sparse(Int32.(vI), Int32.(vJ), vK, mA.m, mA.n);

lldl(mA); #<! Works
lldl(mB); #<! Fails

I get the following error:

julia> lldl(mB)
ERROR: MethodError: no method matching abspermute!(::Vector{Float64}, ::SubArray{Int32, 1, Vector{Int32}, Tuple{UnitRange{Int64}}, true}, ::Int64)

I think the code should support any integer as indices and any float as value. Wasn't that the design goal?

Performance wise, if the input data is 32 Bit having all other computations in 32 but might assist with performance (Memory and calculation throughput).

For the UInt32 case:

mB = sparse(UInt32.(vI), UInt32.(vJ), vK, mA.m, mA.n);
lldl(mB); #<! Fails

The error:

julia> lldl(mB); #<! Fails
ERROR: MethodError: no method matching amd(::SparseMatrixCSC{Float64, UInt32})

It also fails for UInt64.

Remark: I didn't make the matrix symmetric as it is has no significance for the error.

[RoyiAvital]

OK, It seems version 0.4 the added support works correctly.
Moreover:

using SparseArrays;
using MatrixDepot;
using LimitedLDLFactorizations;
using BenchmarkTools;

mA = matrixdepot("poisson", 100);
mB = convert(SparseMatrixCSC{Float64, Int32}, mA);

@btime lldl($mA);
@btime lldl($mB);

Gives me ~15% speedup.

[RoyiAvital]

One more remark, the updated code won't work with UInt32 or UInt64 because amd() doesn't support those (On the C code level).

I wish someone implemented amd() in pure Julia.

[dpo]

@Sacha0 did at some point, but I can't recall the name or status of his repo.

[dpo]

Actually here it is: https://github.com/Sacha0/ApproxMinimumDegree.jl

[RoyiAvital]

That's nice. I wish he continued working on it.
But anyhow, until someone has supported AMD with UInt support there is no real way to have it.