SciML/SparseWithDenseRowColMatrices.jl
View on GitHubSparse plus low-rank dense rows/cols matrices, solved by Sherman-Morrison-Woodbury over PureKLU.jl with symbolic-reuse caching and a LinearSolve.jl interface
- Stars
- 0
- Forks
- 1
- Open beginner issues
- 0
- Indexed issues
- 13
- Avg merge
- 14m
- Merged PRs (30d)
- 6
- Dominant language
- Julia
- License
- MIT
- Last GitHub push
- Sep 17, 2026
- Latest indexed
- Sep 19, 2026
- Contributing guide
- Contributing guide
- Code of conduct
- Code of conduct
- Beginner labels
- No beginner labels indexed
-
Difficulty 4/5 3-5 days Newbie friendliness 52/100
-
Difficulty 2/5 1-3 hours Newbie friendliness 65/100
SciML/SparseWithDenseRowColMatrices.jl#25 · 1 comment ·
-
Difficulty 4/5 3-5 days Newbie friendliness 38/100
-
Difficulty 4/5 3-5 days Newbie friendliness 48/100
-
Difficulty 4/5 3-5 days Newbie friendliness 55/100
-
Difficulty 4/5 3-5 days Newbie friendliness 52/100
-
`lstsq(alg=:iterative)` blanket-rejects non-BLAS eltypes, yet the iterative engine works in BigFloat Open
Difficulty 3/5 1-2 days Newbie friendliness 72/100
-
Difficulty 4/5 3-5 days Newbie friendliness 48/100
-
Difficulty 2/5 1-3 hours Newbie friendliness 75/100
-
Difficulty 4/5 3-5 days Newbie friendliness 55/100
-
Matrix-RHS adjoint/transpose matvec falls through to the generic dense fallback — ~3000x slowdown Open
Difficulty 3/5 1-2 days Newbie friendliness 78/100
-
Difficulty 5/5 Over a week Newbie friendliness 35/100
-
Difficulty 4/5 3-5 days Newbie friendliness 55/100