tf.raw_ops.SelfAdjointEigV2
Computes the eigen decomposition of one or more square self-adjoint matrices.
tf.raw_ops.SelfAdjointEigV2(
input, compute_v=True, name=None
)
Computes the eigenvalues and (optionally) eigenvectors of each inner matrix in input such that input[..., :, :] = v[..., :, :] * diag(e[..., :]). The eigenvalues are sorted in non-decreasing order.
# a is a tensor.
# e is a tensor of eigenvalues.
# v is a tensor of eigenvectors.
e, v = self_adjoint_eig(a)
e = self_adjoint_eig(a, compute_v=False)
| Args | |
|---|---|
input |
A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. Tensor input of shape [N, N]. |
compute_v |
An optional bool. Defaults to True. If True then eigenvectors will be computed and returned in v. Otherwise, only the eigenvalues will be computed. |
name |
A name for the operation (optional). |
| Returns | |
|---|---|
A tuple of Tensor objects (e, v). |
|
e |
A Tensor. Has the same type as input. |
v |
A Tensor. Has the same type as input. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/raw_ops/SelfAdjointEigV2