tf.linalg.qr
Computes the QR decompositions of one or more matrices.
tf.linalg.qr(
input, full_matrices=False, name=None
)
Computes the QR decomposition of each inner matrix in tensor such that tensor[..., :, :] = q[..., :, :] * r[..., :,:])
# a is a tensor.
# q is a tensor of orthonormal matrices.
# r is a tensor of upper triangular matrices.
q, r = qr(a)
q_full, r_full = qr(a, full_matrices=True)
| Args | |
|---|---|
input |
A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. A tensor of shape [..., M, N] whose inner-most 2 dimensions form matrices of size [M, N]. Let P be the minimum of M and N. |
full_matrices |
An optional bool. Defaults to False. If true, compute full-sized q and r. If false (the default), compute only the leading P columns of q. |
name |
A name for the operation (optional). |
| Returns | |
|---|---|
A tuple of Tensor objects (q, r). |
|
q |
A Tensor. Has the same type as input. |
r |
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/linalg/qr