linalg: Jacobi eigenvalues for symmetric matrices
Nobody has claimed this yet.
- Dominant language
- C
- Stars
- 23
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
Add numx_mat_eigenvalues_sym (or similar name) to the linalg module: the Jacobi eigenvalue algorithm for real symmetric matrices, bounded by NUMX_MAX_MAT_ROWS.
Follow CONTRIBUTING.md exactly: full Doxygen header, NULL-checks, numx_status_t return, all four deliverables (header, impl, test, docs page). Test against known eigenvalue examples and verify the trace/determinant invariants hold against the computed eigenvalues.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading CONTRIBUTING.md and locating the linalg module and its existing header, implementation, test, and documentation patterns. Add the symmetric-matrix Jacobi eigenvalue API with the required Doxygen header, NULL checks, and numx_status_t return, then cover known examples plus trace and determinant invariants across the four requested deliverables.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- c
- Domain
- embedded-iot
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 55/100