The QR Decomposition algorithm is, as it's name suggests, a decomposition
algorithm. Given a matrix A, the QR decomposition algorithm factors A into
A = QR where Q is an orthogonal matrix - meaning it's columns form an orthogonal basis - and R is an upper triangular matrix.
The reason I'm implementing this is because it is the base algorithm of least squares regression, and is also used in the QR eigenvalue algorithm, which I plan to implement as well, either in this repo or somewhere else.
This will be a library-type routine, so it will be used via header inclusion. In particular, I'll be recycling a matrix class I have used before verbatim from one of my other repositories, and it is that matrix class (which is very bare bones) that I will use to implement the QR algorithm.