cupy.linalg.svd#

cupy.linalg.svd(a, full_matrices=True, compute_uv=True)[source]#

Singular Value Decomposition.

Factorizes the matrix a as u * np.diag(s) * v, where u and v are unitary and s is an one-dimensional array of a’s singular values.

Parameters:

a (cupy.ndarray) – The input matrix with dimension (..., M, N).
full_matrices (bool) – If True, it returns u and v with dimensions (..., M, M) and (..., N, N). Otherwise, the dimensions of u and v are (..., M, K) and (..., K, N), respectively, where K = min(M, N).
compute_uv (bool) – If False, it only returns singular values.

Returns:

A tuple of (u, s, v) such that a = u * np.diag(s) * v.

Return type:

tuple of cupy.ndarray

Warning

This function calls one or more cuSOLVER routine(s) which may yield invalid results if input conditions are not met. To detect these invalid results, you can set the linalg configuration to a value that is not ignore in cupyx.errstate() or cupyx.seterr().

Note

On CUDA, when a.ndim > 2 and the matrix dimensions <= 32, a fast code path based on Jacobian method (gesvdj) is taken. Otherwise, a QR method (gesvd) is used.

On ROCm, there is no such a fast code path that switches the underlying algorithm.