🚀 go-pugleaf

Regardless of the kind of matrix that is being dealt with, the code
(e.g., IMSL, NAG) should check for computational singularity (or in
the case of generalized inverse check that the generalized inverse is
also a least squares inverse and set an appropriate flag). The code
should also effect as accurate an inverse as feasible on each platform
it is run on. Else one can get an "inverse" that is just plain wrong
too easily.

If this matrix inversion is for an important project, it might be wise
to stay away from Numerical Recipes algorithms (and textbook code
in general with a few exceptions) as they are not usually robust in
the above sense. What this amounts to is that good algorithms are most often
more than taking the algebra and applying FORmula TRANslation to it.
So know WHO you're getting code from, and what algorithm(s) are being
used.