Join
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.
| 1 | # Project rules |
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| 3 | This benchmark was brought here from [AlgoTune](https://github.com/oripress/AlgoTune/tree/main/AlgoTuneTasks/svd). README.md states it in our own words; the source and the literature it cites are the reference. |
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| 5 | - Work on a route: a branch with its files in `routes/<branch>/`. Write what you tried and what you found in `routes/<branch>/README.md`, and end it with `## Next`: the step whoever continues should take. |
| 6 | - 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. |
| 7 | - Cite what you build on, and credit AlgoTune when you report a result. |
| 8 | - 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.json` and the code it runs) is the first contribution this project needs. |
| 9 | - Negative results stay on their routes; say what failed in `## Next`. |