Daniel Karrasch

That seems fixed on nightly/v1.9: ```julia julia> using SparseArrays julia> x = SparseVector{Int, UInt8}(6, UInt8[1], [-1]) 6-element SparseVector{Int64, UInt8} with 1 stored entry: [1] = -1 julia> sum(x) -1 julia>...

So I finally took a look at SparseExtra.jl. It's very nice that it composes with outer wrappers! What seems to be currently missing is that for `Symmetric` and `Hermitian`, the...

The issue with the given problem is that the sparse matrix is recognized as Hermitian, so it gets wrapped as `Hermitian(A)`. The next step would be that SuiteSparse.jl takes over,...

I'll second on that. This is one of the reasons why it's very much worth considering representing products of sparse matrices lazily as, for instance, in LinearMaps.jl.

Returning `opnorm(Array(A))` silently and by default might be really bad because of massive memory consumption, and should be done consciously by the user. I think that's the rationale for the...

I'm no authority here, but I guess this is a bit out of scope to provide one standard way to do it. You could proceed as you said, compute the...

This may have to do with the "compactification" of sparse matrices. IIUC, it's not true that any dot corresponds to a single scalar entry, but you first coarsen the matrix...

Sorry, I messed something up. I guess this functionality never existed anyway. I wonder how one could avoid the confusion. The docstring of `ldiv!` emphasizes that the middle argument should...

In a sense, you can call the (generic) three argument `ldiv!` (from LinearAlgebra/src/factorization.jl), it's just that it calls the two argument `ldiv!` internally, which does not exist for the sparse...

I started working on this. I realized that both QRsparse and CHOLMOD are missing 3-args `ldiv!` methods. From what I understand, there is no truly in-place solve method for these...