问题描述
SciPy和Numpy都内置了用于奇异值分解(SVD)的函数.这些命令基本上是scipy.linalg.svd和numpy.linalg.svd.两者有什么区别?他们中的任何一个都比另一个更好吗?
Both SciPy and Numpy have built in functions for singular value decomposition (SVD). The commands are basically scipy.linalg.svd and numpy.linalg.svd. What is the difference between these two? Is any of them better than the other one?
推荐答案
来自常见问题页面，它说scipy.linalg子模块为Fortran LAPACK库提供了更完整的包装器，而numpy.linalg试图能够独立于LAPACK进行构建.
From the FAQ page, it says scipy.linalg submodule provides a more complete wrapper for the Fortran LAPACK library whereas numpy.linalg tries to be able to build independent of LAPACK.
对于svd的不同实现，我做了一些基准函数，发现scipy.linalg.svd比numpy对应的更快:
I did some benchmarks for the different implementation of the svd functions and found scipy.linalg.svd is faster than the numpy counterpart:
但是， jax 包裹了numpy，又名jax.numpy.linalg.svd甚至更快:
However, jax wrapped numpy, aka jax.numpy.linalg.svd is even faster:
可以在此处找到完整的基准测试笔记本.
Full notebook for the benchmarks are available here.
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