Yet another snappy blog post title!
The sub-theme for this one is that by rethinking a problem in a different way, we can squeeze the solution into far fewer resources.
In this case, I'd recently been doing some work (at work) which involved fitting a quadratic curve to a set of noisy datapoints (because they're derived from ADCs, which... often need a lot of filtering to get decent results despite their nominal 12-bit accuracy).
In practice I found a website that covered the Least Squares Quadratic fitting algorithm.
The interesting thing for me is that this algorithm is explained in quite a lengthy webpage; and on top of that there's a whole side box that allows you to enter a set of (x,y) coordinates and it'll figure out the quadratic coefficients (and the correlation).
That's quite a lot of resources, which implies it's fairly involved, but is it really? Looking at the equations and how the coefficients are derived from them: