tf.linalg.eigvalsh

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Computes the eigenvalues of one or more self-adjoint matrices.

Aliases:

  • tf.compat.v1.linalg.eigvalsh
  • tf.compat.v1.self_adjoint_eigvals
  • tf.compat.v2.linalg.eigvalsh
tf.linalg.eigvalsh(
    tensor,
    name=None
)

Note: If your program backpropagates through this function, you should replace it with a call to tf.linalg.eigh (possibly ignoring the second output) to avoid computing the eigen decomposition twice. This is because the eigenvectors are used to compute the gradient w.r.t. the eigenvalues. See _SelfAdjointEigV2Grad in linalg_grad.py.

Args:

  • tensor: Tensor of shape [..., N, N].
  • name: string, optional name of the operation.

Returns:

  • e: Eigenvalues. Shape is [..., N]. The vector e[..., :] contains the N eigenvalues of tensor[..., :, :].

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