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## Summary Add a specialized `isdiag` method for `AbstractSparseMatrixCSC` that traverses the CSC structure directly in O(nnz) time, compared to the generic fallback which is O(n²) for an n×n matrix. **Changes:** - Import `isdiag` from LinearAlgebra - Add `isdiag(A::AbstractSparseMatrixCSC)` that checks diagonal property by traversing the sparse structure - Add comprehensive tests for `isdiag` on sparse matrices ## Motivation This addresses performance issues in packages like OrdinaryDiffEq.jl where `isdiag` checks on large sparse mass matrices caused severe initialization delays. For large DAE systems (e.g., 259k variables), this reduces initialization time from ~60 seconds to <1ms. Reference: SciML/OrdinaryDiffEq.jl#3011 ## Benchmarks ``` n=1000: generic=0.00ms, efficient=0.00ms n=10000: generic=0.04ms, efficient=0.00ms, speedup=12626x n=50000: generic=0.19ms, efficient=0.00ms, speedup=47511x n=100000: generic=0.39ms, efficient=0.00ms, speedup=96737x ``` The efficient implementation runs in essentially constant time since it only needs to check the stored elements. ## Test plan - Added `isdiag` tests in `test/sparsematrix_ops.jl`: - Diagonal matrices (sparse diagonal, empty matrix) - Non-diagonal matrices (tridiagonal, bidiagonal, full) - Non-square matrices - Consistency with dense `isdiag` - Explicit zeros on off-diagonal (should still be diagonal) 🤖 Generated with [Claude Code](https://claude.ai/code) --------- Co-authored-by: ChrisRackauckas-Claude <noreply@anthropic.com> Co-authored-by: Chris Rackauckas <accounts@chrisrackauckas.com>
Create a unified initialization function for all SuiteSparse libraries (both in the stdlib and elsewhere like KLU). This remains lazily initialized. Should address #671. I wonder whether this should be truly `public` but KLU, AMD, and ParU, SPEX will rely on this to function correctly so probably should be. Also bumps wrapper generation. @gbaraldi not sure how to test this with your PR but tests on MinFEM.jl seem to pass locally for me.
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indtypeforSparseMatrixCSCColumnSubset#700HermOrSymsparse matrix times dense matrix #692mul!forHermOrSymsparse matrices #689