AlgoTune SciPy speed-ups

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.

Task

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.

How to take part

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.