Solves systems of linear eqns A X = RHS
, given LU factorizations.
View aliases
Compat aliases for migration
See Migration guide for more details.
tf.linalg.lu_solve(
lower_upper, perm, rhs, validate_args=False, name=None
)
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').
|
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