Nonlinear least squares / Fish burrito
When you devote almost all waking energy to our startup, it's not uncommon to find yourself eating and working simultaneously. Today the dynamic duo is a fish burrito and nonlinear least squares regression (NLLS).

While the burrito is still hot, let's dive into a little compare/contrast.
Many of you may not be familiar with one of these items, so I'll give that item a quick introduction: A fish burrito is a mexican entree fashioned most typically out of fish, lettuce, a tortilla, and an interstitial sauce of some kind.
Now that we've covered that, you are probably thinking, man, that's almost identical to NLLS.
And you're right.
- Both the burrito and the mathematical technique are customizable.
- Both are subject to local maxima -- if you start looking for a fish burrito in the wrong town, you will end up with the best of the worst. NLLS will end up with with a solution near the initial guess, but not necessarily the best overall. Overcoming this problem, in both cases, can be done by randomly jumping around periodically (with an airplane and random number generator, respectively).
- When someone says "fish burrito," there's obviously a lot of ambiguity. It could mean an authentic, south-of-the-border creation -- a homemade fish burrito fashioned from leftovers -- or, as in today's case, a fish burrito from Illegal Pete's. Saying "Nonlinear least squares" is equally unspecific. Everyone's left wondering, do you mean a gradient-descent NLLS? Do you mean MATLAB's
lsqnonlinfunction? Or, as in today's case, do you mean the Levenberg-Marquardt algorithm?
Despite these similarities, we can't end without pointing out the key difference:
- Fish burritos do not require an estimate of the Jacobian.



