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Add complex number support to linalg.svdvals #562

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Dec 14, 2022
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Add complex number support to linalg.svdvals #562

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6 changes: 4 additions & 2 deletions spec/API_specification/array_api/linalg.py
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Expand Up @@ -421,15 +421,17 @@ def svdvals(x: array, /) -> array:
"""
Returns the singular values of a matrix (or a stack of matrices) ``x``.

When ``x`` is a stack of matrices, the function must compute the singular values for each matrix in the stack.

Parameters
----------
x: array
input array having shape ``(..., M, N)`` and whose innermost two dimensions form matrices on which to perform singular value decomposition. Should have a real-valued floating-point data type.
input array having shape ``(..., M, N)`` and whose innermost two dimensions form matrices on which to perform singular value decomposition. Should have a floating-point data type.

Returns
-------
out: array
an array with shape ``(..., K)`` that contains the vector(s) of singular values of length ``K``, where ``K = min(M, N)``. For each vector, the singular values must be sorted in descending order by magnitude, such that ``s[..., 0]`` is the largest value, ``s[..., 1]`` is the second largest value, et cetera. The first ``x.ndim-2`` dimensions must have the same shape as those of the input ``x``. The returned array must have the same real-valued floating-point data type as ``x``.
an array with shape ``(..., K)`` that contains the vector(s) of singular values of length ``K``, where ``K = min(M, N)``. For each vector, the singular values must be sorted in descending order by magnitude, such that ``s[..., 0]`` is the largest value, ``s[..., 1]`` is the second largest value, et cetera. The first ``x.ndim-2`` dimensions must have the same shape as those of the input ``x``. The returned array must have a real-valued floating-point data type having the same precision as ``x`` (e.g., if ``x`` is ``complex64``, the returned array must have a ``float32`` data type).
"""

def tensordot(x1: array, x2: array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> array:
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