tf.linalg.lu_matrix_inverse
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Computes the inverse given the LU decomposition(s) of one or more matrices.
tf . linalg . lu_matrix_inverse (
lower_upper , perm , validate_args = False , name = None
)
This op is conceptually identical to,
inv_X = tf . lu_matrix_inverse ( * tf . linalg . lu ( X ))
tf . assert_near ( tf . matrix_inverse ( X ), inv_X )
# ==> True
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)
.
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_matrix_inverse').
Returns
inv_x
The matrix_inv, i.e.,
tf.matrix_inverse(tf.linalg.lu_reconstruct(lu, perm))
.
Examples
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
x = [[[ 3. , 4 ], [ 1 , 2 ]],
[[ 7. , 8 ], [ 3 , 4 ]]]
inv_x = tf . linalg . lu_matrix_inverse ( * tf . linalg . lu ( x ))
tf . assert_near ( tf . matrix_inverse ( x ), inv_x )
# ==> True
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License , and code samples are licensed under the Apache 2.0 License . For details, see the Google Developers Site Policies . Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2020-10-01 UTC.
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