My last post was about a fairly esoteric new language feature in python: coroutines. I wasn't reading up on them because they're whiz-bang cool (okay, not only because they're whiz-bang cool) but because I have an actual problem that they might help with. For the triple system, I find I need to do very slow numerical optimization problems. That is, the objective function takes seconds to evaluate. It can't be parallelized internally, and numerical minimization is largely a sequential operation: evaluate the function, figure out which way is downhill, go there, repeat. So this code currently uses just one core, and is almost as slow as the highly parallel Markov-Chain Monte Carlo code that does hundreds of times as much work. So obviously it would be nice to find some parallelism. I have a way to do that: parallelize gradient evaluations.
The other reason for this post is to make a point about code robustness. Getting numerical derivatives right is a notoriously difficult process, and getting them both efficient and right is even harder. The package I have been using, MINUIT, has been used by the particle physicists for decades, and they have hammered out all sorts of corner cases that a naive implementation might miss. So I'm not going to use a naive implementation, not least because my problem is numerically difficult. This means the code is not simple, and I don't want to try to restructure it manually to be more concurrent; I want the computer to handle the concurrency for me.
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