Function ldlt_decomposition_tensor 

pub fn ldlt_decomposition_tensor<T, S, I>(
    tensor: I,
) -> Option<LDLTDecompositionTensor<T>>
where
    T: Numeric,
    for<'a> &'a T: NumericRef<T>,
    I: Into<TensorView<T, S, 2>>,
    S: TensorRef<T, 2>,

Computes the LDL^T decomposition of a matrix. This yields a matrix L and a matrix D such that for the provided matrix A, L * D * L^T = A. L will always be unit lower triangular, ie all entries above the diagonal will be 0, and all entries along the diagonal will br 1. D will always contain zeros except along the diagonal. This decomposition is closely related to the cholesky decomposition with the notable difference that it avoids taking square roots.

Similarly to the cholseky decomposition, the input matrix must be symmetric and positive definite.

This function does not check that the provided matrix is symmetric. However, given a symmetric input, if the input is only positive semidefinite None is returned. In the future additional checks that the input is valid could be added.

The shapes of the L and D matrices will be the same as the input matrix.

Warning

With some uses of this function the Rust compiler gets confused about what type T should be and you will get the error:

overflow evaluating the requirement &'a _: easy_ml::numeric::NumericByValue<_, _>

In this case you need to manually specify the type of T by using the turbofish syntax like: linear_algebra::ldlt_decomposition_tensor::<f32, _, _>(&tensor)