svd(a, coerce_signs=True)
Whether or not to apply sign coercion to singular vectors in order to maintain deterministic results, by default True.
Left-singular vectors of a (in columns) with shape (M, K) where K = min(M, N).
Singular values of a.
Right-singular vectors of a (in rows) with shape (K, N) where K = min(M, N).
Compute the singular value decomposition of a matrix.
da.linalg.svd_compressed
Randomized SVD for fully chunked arrays
dask.array.linalg.tsqr
QR factorization for tall-and-skinny arrays
dask.array.utils.svd_flip
Sign normalization for singular vectors
np.linalg.svd
Equivalent NumPy Operation
>>> u, s, v = da.linalg.svd(x) # doctest: +SKIPSee :
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dask.array.linalg.tsqr
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