Matrix inversion is a classical problem, and can be very complicated for large matrices. There are many ways to simplify this for special types of matrices. Among them, one is to transform the matrix into a set of upper or lower triangular matrices. Consider our target matrix

which is

Hermitian and

positive-definite. Such matrices are quite famous and an example is the covariance matrix in statistics. It’s inverse is seen in the Gaussian probability density function for vectors. Then,

Cholesky decomposition breaks

where is a lower triangular matrix, while is an upper triangular matrix.

It is much easier to compute the inverse of a triangular matrix and there exist numerical solutions. Then the original matrix inverse is computed simply by multiplying the two inverses as

As bonus, the determinant is also much easier to compute.

One can also use complex matrices, and just use a conjugate-transpose instead of transpose alone.

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