This PR is mostly a FYI regarding JuliaArrays/StaticArrays.jl#913 Depending on whether or not that issue is closed, you may want to switch to explicitly calling `generic_norm2` on static vectors to circumvent the accuracy issue. The issue arises when combining static arrays with ForwardDiff, in our case it occured in `exp(::SkewSymmetric)`. I did some benchmarking, and `generic_norm2` is faster than `norm` for all standard arrays up to at least length 9. For static arrays, it appears to do okay as well, while avoiding the accuracy issue. ```julia julia> a 3-element SVector{3, Int64} with indices SOneTo(3): 1 2 3 julia> @Btime norm($(Ref(a))[]) # standard norm of static array 5.135 ns (0 allocations: 0 bytes) 3.74166 julia> @Btime norm($(Ref(Vector(a)))[]) # standard norm of array 8.545 ns (0 allocations: 0 bytes) 3.74166 julia> @Btime LinearAlgebra.generic_norm2($(Ref((a)))[]) # generic_norm2 of static array 4.490 ns (0 allocations: 0 bytes) 3.74166 julia> @Btime LinearAlgebra.generic_norm2($(Ref(Vector(a)))[]) # generic_norm2 of array 7.671 ns (0 allocations: 0 bytes) 3.74166 ```

Commits

Commits on Nov 6, 2024

Fix GitHub CI

Use numerically robust norm

Add WrenchesCache and bump version to 2.5.0

Update benchmark results

Add `WrenchesCache` and bump version to 2.5.0