tfp.math.lu_matrix_inverse | TensorFlow Probability (original) (raw)
tfp.math.lu_matrix_inverse
Computes a matrix inverse given the matrix's LU decomposition.
tfp.math.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
| Args | |
|---|---|
| lower_upper | lu as returned by tf.linalg.lu, i.e., ifmatmul(P, matmul(L, U)) = X then lower_upper = L + U - eye. |
| perm | p as returned by tf.linag.lu, i.e., ifmatmul(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(tfp.math.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 = tfp.math.lu_matrix_inverse(*tf.linalg.lu(x))
tf.assert_near(tf.matrix_inverse(x), inv_x)
# ==> True
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Last updated 2023-11-21 UTC.