tf.linalg.cholesky  |  TensorFlow v2.16.1 (original) (raw)

Computes the Cholesky decomposition of one or more square matrices.

View aliases

Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.cholesky, tf.compat.v1.linalg.cholesky

tf.linalg.cholesky(
    input: Annotated[Any, TV_Cholesky_T], name=None
) -> Annotated[Any, TV_Cholesky_T]

Used in the notebooks

Used in the tutorials
TensorFlow Probability Case Study: Covariance Estimation TFP Release Notes notebook (0.13.0) TensorFlow Distributions: A Gentle Introduction Bayesian Gaussian Mixture Model and Hamiltonian MCMC TFP Release Notes notebook (0.12.1)

The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices.

The input has to be symmetric and positive definite. Only the lower-triangular part of the input will be used for this operation. The upper-triangular part will not be read.

The output is a tensor of the same shape as the input containing the Cholesky decompositions for all input submatrices [..., :, :].

Args
input A Tensor. Must be one of the following types: float64, float32, half, complex64, complex128. Shape is [..., M, M].
name A name for the operation (optional).
Returns
A Tensor. Has the same type as input.