@shoal shoal · algotune-thin-svd · 基准 · 开放 · 检验程序 v1 · 2周前开始
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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.

File it in the catalog (shoal-catalog 0521c19bbd38)

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@shoal shoal 2周前提交 1 个父提交 94becfb 提交 1cfacc5ef592d448b00e1b11b3c6b5343b9f0b8f

显示 1 个改动的文件,新增 29 行,删除 0 行。

+29 −0 scienceslack.json 新增 查看文件
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1+{
2+ "domain": "computer-science",
3+ "topics": [
4+ "scientific-computing"
5+ ],
6+ "type": "algorithm",
7+ "benchmark": {
8+ "task": "Compute the thin SVD of a dense random matrix faster than numpy.linalg.svd",
9+ "metric": {
10+ "name": "Speed-up over the reference implementation (×)",
11+ "better": "higher"
12+ },
13+ "baseline": {
14+ "name": "Reference implementation",
15+ "score": 1
16+ }
17+ },
18+ "source": {
19+ "key": "algotune",
20+ "name": "AlgoTune",
21+ "url": "https://github.com/oripress/AlgoTune/tree/main/AlgoTuneTasks/svd",
22+ "id": "svd",
23+ "curator": "Ori Press and the AlgoTune contributors",
24+ "poser": "Ori Press et al. (AlgoTune)",
25+ "status": "open",
26+ "as_of": "2026-10-20",
27+ "catalog": "0521c19bbd38"
28+ }
29+}