For this recipe, we will implement a matrix decomposition method for linear regression. Specifically, we will use Cholesky decomposition, for which relevant functions exist in TensorFlow.

# Implementing a decomposition method

# Getting ready

Implementing the inverse methods from the previous recipe can be numerically inefficient in most cases, especially when the matrices get very large. Another approach is to decompose the `A` matrix and perform matrix operations on the decompositions instead. One such approach is to use the built-in Cholesky decomposition method in TensorFlow.

One reason people are so interested in decomposing a matrix into more matrices is that the resultant matrices will have assured properties that allow us...