tf.linalg.lu_solve
Solves systems of linear eqns A X = RHS, given LU factorizations.
tf.linalg.lu_solve(
lower_upper, perm, rhs, validate_args=False, name=None
)
Note: this function does not verify the implied matrix is actually invertible nor is this condition checked even when validate_args=True.
| Args | |
|---|---|
lower_upper |
lu as returned by tf.linalg.lu, i.e., if matmul(P, matmul(L, U)) = X then lower_upper = L + U - eye. |
perm |
p as returned by tf.linag.lu, i.e., if matmul(P, matmul(L, U)) = X then perm = argmax(P). |
rhs |
Matrix-shaped float Tensor representing targets for which to solve; A X = RHS. To handle vector cases, use: lu_solve(..., rhs[..., tf.newaxis])[..., 0]. |
validate_args |
Python bool indicating whether arguments should be checked for correctness. Note: this function does not verify the implied matrix is actually invertible, even when validate_args=True. Default value: False (i.e., don't validate arguments). |
name |
Python str name given to ops managed by this object. Default value: None (i.e., 'lu_solve'). |
| Returns | |
|---|---|
x |
The X in A @ X = RHS. |
Examples
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
x = [[[1., 2],
[3, 4]],
[[7, 8],
[3, 4]]]
inv_x = tf.linalg.lu_solve(*tf.linalg.lu(x), rhs=tf.eye(2))
tf.assert_near(tf.matrix_inverse(x), inv_x)
# ==> True
© 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/lu_solve