Left Matrix Inverses in R

The following question popped up on OR Stack Exchange: given an $m\times n$ matrix $A$ with $m > n,$ how can one find all left inverses of $A$ in R? The author mentions that dimensions in their case would be around 200x10.

A left inverse is a matrix $B\in \mathbb{R}^{n\times m}$ such that $B A = I.$ In order for a left inverse to exist, $A$ must have full column rank. If it does not, $Ax = 0$ for some nonzero $x\in \mathbb{R}^n,$ in which case $$x = Ix = (BA)x = B(Ax) = B\cdot 0 = 0,$$ a contradiction.

Henceforth we assume that $A$ has full column rank, in which case the left null space $\mathcal{N} \subseteq \mathbb{R}^m$ will have dimension $m-n.$ Now suppose that $C\in \mathbb{R}^{n\times m}$ has the property that every row of $C$ belongs to $\mathcal{N}.$ Then $CA = 0$ and so $(B+C)A = BA+0 = I,$ making $B+C$ another left inverse of $A.$ That means $A$ has an uncountably infinite number of left inverses. Conversely, if $DA=I$ then the rows of $D-B$ belong to $\mathcal{N}.$ So the set of left inverses of $A$ can be fully characterized by any individual left inverse and a basis for the left null space of $A.$

Getting this information in R is remarkably easy. There are multiple ways to compute a left inverse, but if you have the pracma library loaded, then the function pracma::pinv() can be used to compute the Moore-Penrose pseudoinverse, which is a left inverse of $A.$ To get a basis for the left null space of $A,$ we apply the function pracma::nullspace() to $A^T,$ which computes a basis for the right null space of the transpose of $A,$ and then transpose the results.

I have a small R notebook that demonstrates this on a random 200x10 matrix.