Mistake in the documentation of numpy.linalg.svd
See original GitHub issueI believe there is a minor mistake in the numpy.linalg.svd
documentation, since “Hermitian” should be in the parameters, not in the outputs.
https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.svd.html
Issue Analytics
- State:
- Created 4 years ago
- Comments:11 (7 by maintainers)
Top Results From Across the Web
numpy.linalg.svd — NumPy v1.24 Manual
Compute singular values of a matrix. Notes. Changed in version 1.8.0: Broadcasting rules apply, see the numpy.linalg documentation ...
Read more >Incorrect documentation for jax.numpy.linalg.svd #8414 - GitHub
The documentation shows that jax.numpy.linalg.svd accepts the keyword argument hermitian , like the numpy.linalg version.
Read more >the numpy SVD decomposition docstring is wrong - Sage Trac
The statement that S is the diagonal matrix np.diag(s) is just totally false if the input matrix a is nonsquare, since S is...
Read more >numpy.linalg.svd not returning Sigma in descending order
The documentation and examples that I've found all seem to indicate that the Sigma values that are returned are ordered in descending order...
Read more >numpy.linalg.svd() - JAX documentation
When a is a 2D array, and full_matrices=False , then it is factorized as u @ np.diag(s) @ vh = (u * s)...
Read more >
Top Related Medium Post
No results found
Top Related StackOverflow Question
No results found
Troubleshoot Live Code
Lightrun enables developers to add logs, metrics and snapshots to live code - no restarts or redeploys required.
Start Free
Top Related Reddit Thread
No results found
Top Related Hackernoon Post
No results found
Top Related Tweet
No results found
Top Related Dev.to Post
No results found
Top Related Hashnode Post
No results found
The bug is in the 1.17 docs:
https://numpy.org/doc/1.17/reference/generated/numpy.linalg.svd.html
We might want to rebuild these from the last 1.17 release, I think right now they’re a bit behind. It might make no difference here though.
I think we can close the issue now. @charris thanks for the explanation.