Daniel Karrasch

One potential concern could be accuracy. If you insert additional steps, then each comes with its own condition number and adds to the overall error accumulation. What comes to my...

I suspect this is a matter of documentation, like saying that `isposdef(E::Eigen)` only checks for real and positive eigenvalues, assuming that `E` is the eigendecomposition of a self-adjoint matrix ("`RealHermOrSymComplexHerm`")....

Great, seems like two good reasons to make a breaking change: remove `PDSparseMat` and for whatever remains make SparseArrays.jl a weak dependency? 😛

Now that the lower compat has been raised to v1.10, this would be worthwhile to revisit and finish. I don't have current loading/latency times, but at the time this would...

Perhaps you will want to replace `fill(i, N)` and alike by `ntuple(_ -> i, N)`. That makes the allocations disappear.

I'm finding the metaprogramming stuff quite hard to read. A simple ```julia using LinearAlgebra import LinearAlgebra: checksquare function checksquare(A::AbstractArray{ i, N)...] end t end ``` performs even better (due to...

@aravindh-krishnamoorthy You may be interested in this. I saw you're working on efficient square roots of matrices.

Without going through the list in detail, I suspect that most of it is intentional, though the intention may be misguided. In that case a concrete argument/use case would be...

But aren't both representations/shapes isometries? The current behaviour is very old Julian behaviour. `Matrix(q)` may yield non-square, `collect(q)` always returns the square version of it. For many operations, you need...

Sure, but Householder reflections are naturally square, so you get the square matrix representation out of the very same data. As for the `Matrix(q')` case, that is debatable. Traditionally, we...