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更正svd、svdvals、svd_lowrank文档公式错误 #70995

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更正svd、svdvals、svd_lowrank文档公式错误 #70995

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07f8a95
Update linalg.py
aquagull Jan 26, 2025
58ccebd
Update linalg.py
aquagull Jan 26, 2025
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8 changes: 4 additions & 4 deletions python/paddle/tensor/linalg.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2930,7 +2930,7 @@ def svd(
Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:

.. math::
X = U * diag(S) * VT
X = U * diag(S) * V^{H}

Args:
x (Tensor): The input tensor. Its shape should be `[..., N, M]`,
Expand Down Expand Up @@ -3010,7 +3010,7 @@ def svdvals(x: Tensor, name: str | None = None) -> Tensor:
produced by singular value decomposition:

.. math::
X = U * diag(S) * VH
X = U * diag(S) * V^{H}

Args:
x (Tensor): The input tensor. Its shape should be `[..., M, N]`, where
Expand Down Expand Up @@ -3091,12 +3091,12 @@ def svd_lowrank(
If :math:`X` is the input matrix or a batch of input matrices, the output should satisfies:

.. math::
X \approx U * diag(S) * V^{T}
X \approx U * diag(S) * V^{H}

When :math:`M` is given, the output should satisfies:

.. math::
X - M \approx U * diag(S) * V^{T}
X - M \approx U * diag(S) * V^{H}

Args:
x (Tensor): The input tensor. Its shape should be `[..., N, M]`, where `...` is
Expand Down
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