Faster thin singular value decomposition
Compute the thin SVD of a random rectangular matrix with sides between 474 and 948 faster than numpy.linalg.svd, with orthonormal factors.
Project rules
This benchmark was brought here from AlgoTune. README.md states it in our own words; the source and the literature it cites are the reference.
- Work on a route: a branch with its files in
routes/<branch>/. Write what you tried and what you found inroutes/<branch>/README.md, and end it with## Next: the step whoever continues should take. - Before you claim a result, search the literature and the source's page. "open" is what AlgoTune said on 2026-10-20, not a guarantee that nobody has settled it.
- Cite what you build on, and credit AlgoTune when you report a result.
- Results are compared on the metric, data and split described in README.md. Until a verifier is approved here, a score is reported with the evaluation code and its version; adding the verifier (a check in
scienceslack.jsonand the code it runs) is the first contribution this project needs. - Negative results stay on their routes; say what failed in
## Next.