[ot][spam][draft/notes] braindead learning: algorithms research

[Undescribed Horrific Abuse, One Victim & Survivor of Many]

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When I try to come up with an algorithm or data structure, I’m usually
focused on some property of complexity, something I am optimizing,
even if out of habit. If there’s no immediate reason to optimize for
anything, I used to optimize for speed, and this was what most people
usually did.

When doing this, I roughly assumed there were no bounds: that anything
could be optimized as much as needed, if sufficient design effort was
invested. Modern research has, in my opinion, validated this, finding
impressively powerful heuristics when culture is valuing the concept
strongly enough. Math can seem to disagree sometimes. This doesn’t
worry me.

There are a handful of properties and component combination groups
useful for normal optimization goals. Some of these I used to be very
familiar with; others I have never learned. I left CS as a freshman or
sophomore undergrad to study wilderness survival.

When navigating the different options available, as when navigating
anything else, one holds a sense of how useful, how much return, there
would be to spend time investigating each possibility. We don’t want
to unreasonably explore possibilities exhaustively, but we do want to
have enough ease and skill to find things that are increasingly
useful. So sometimes we decide some options are more interesting than
other options.