Mateusz Baran

Anyway, showing the value is the issue, not computing `lu`.

The exact result of matrix multiplication depends on the exact code emitted by LLVM. StaticArrays.jl relies on its auto-vectorization features, and the exact code may also depend on what exactly...

LinearAlgebra is trying to factorize symmetric matrices for inversion using the Bunch-Kafuman algorithm which is not available in StaticArrays.jl. You can either just use LinearAlgebra and accept the non-ideal performance,...

> > Implement Bunch-Kafuman for static arrays > > Presumably this is preferred? Yes, probably, though so far no one has made a PR. > > Drop the Symmetric wrapper...

I'm actually thinking about extending `Simple` covariance to all cases covered by `cov` from StatsBase.jl. It would be nice to have a common interface for all methods. All other algorithms...

StaticArrays.jl isn't generally designed to work with such arrays. Very few operations on `StaticArray`s with more than 100 elements are faster than their non-statically sized counterparts. Of course it's possible...

Note that `[x->x*g for g∈[m]]` doesn't actually do any multiplication by itself, it makes a `Vector` of anonymous functions. Also repeating this line creates new anonymous functions that must be...

Currently there is no such functionality but I can add it for https://github.com/JuliaArrays/ElasticArrays.jl wrapped in `HybridArray`s. Would that work for you?

Hm, that broadcast shouldn't be so slow but I'm not sure what causes that issue. I'll have to investigate.

I've spent some time investigating but I got stuck: https://discourse.julialang.org/t/tracking-the-cause-of-allocation-in-the-presence-of-a-generated-function/98215 . I have no idea why Julia failed to optimize that call and the tools I know don't help :confused:...