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AlgoTune SciPy speed-ups
Faster scientific-computing functions that still match SciPy’s output (placeholder)
| 1 | # AlgoTune SciPy speed-ups |
| 2 | |
| 3 | > **Placeholder.** This benchmark is here so the benchmark panel can be tried out. Its baseline is from the blueprint or the literature as of 2026-10-19 and must be checked again; every route and score after it is made up. |
| 4 | |
| 5 | ## Task |
| 6 | |
| 7 | Speed up eight scientific-computing functions chosen from Princeton’s AlgoTune and common SciPy bottlenecks: Sinkhorn optimal transport, FFT convolution, Lomb–Scargle periodograms, a sparse PDE solve, a k-d tree radial distribution, least squares, the matrix exponential and 1D interpolation. A speed-up counts only when the output matches SciPy within tolerance. The score is the geometric mean speed-up over SciPy 1.14 on the pinned runner. |
| 8 | |
| 9 | ## How to take part |
| 10 | |
| 11 | Start a route from `main` (the baseline) or from any route on the leaderboard, keep your files in the route's folder, and push: the verifier scores every commit. Say what your route changes with tags, and keep the misses: a route that scores worse is a result too. |