Doc update for complex numbers#51129
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mruberry
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mruberry
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@@ Coverage Diff @@
## gh/anjali411/90/base #51129 +/- ##
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- Coverage 80.88% 80.54% -0.35%
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Files 1931 1931
Lines 210560 210560
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- Hits 170311 169593 -718
- Misses 40249 40967 +718 |
albanD
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Jan 27, 2021
| Spectral operations (e.g., :func:`torch.fft`, :func:`torch.stft` etc.) currently don't use complex tensors but | ||
| the API will be soon updated to use complex tensors. | ||
| Spectral operations in the `torch.fft module <https://pytorch.org/docs/stable/fft.html#torch-fft>`_ support | ||
| native complex tensors. |
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nit: "complex tensors natively"?
| functions soon: :func:`torch.matmul`, :func:`torch.solve`, :func:`torch.eig`, | ||
| :func:`torch.symeig`. If any of these would help your use case, please | ||
| `search <https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3Aopen+complex>`_ | ||
| Many linear algebra operations, like :func:`torch.matmul`, :func:`torch.svd`, :func:`torch.solve` etc., support complex numbers. |
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Should the , be before the etc.?
| you get the regular complex gradient. For :math:`C → R` real-valued loss functions, | ||
| `grad.conj()` gives a descent direction. For more details, check out the note :ref:`complex_autograd-doc`. | ||
| PyTorch supports autograd for complex tensors. The gradient computed is the Conjugate Wirtinger derivative, | ||
| the negative of which is precisely the direction of steepest descent used in Gradient Descent algorithm. Thus, |
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nit: that should be used in Gradient Descent
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@anjali411 merged this pull request in fd9a85d. |
laurentdupin
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Apr 24, 2026
Summary: Pull Request resolved: pytorch#51129 Test Plan: Imported from OSS Reviewed By: pbelevich Differential Revision: D26094947 Pulled By: anjali411 fbshipit-source-id: 4e1cdf8915a8c6a86ac3462685cdce881e1bcffa
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Differential Revision: D26094947