this post was submitted on 25 Jun 2025
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Complexity classes, specifically the ones for algorithms taking asymptotically linear time in the size of their input. One is deterministic, the other nondeterministic.
Other famous classes like L, P or EXP are trivially in their nondeterministic equivalents, but it's unknown if they're strict subsets. In the case of P it's one of the Clay problems. LIN is the main (only?) case where the answer is known, and it's positive.
An example of a problem that's in NP, but we've kind of bet our civilisation on not being in P, is finding a string with a given hash value. It's a function, it gives the same answer every time, but it's preimages are (we think) completely intractable. What I've described as a possible concrete example is a kind of linear-time version of a hash algorithm. That's less useful, but apparently the (linear-time) irreversibility can be proven in that case.
Thanks for the explanation!
I'm familiar with O() notation, but hadnt seen LIN before, which would be O(1). But that may be because I stick more to the papers written for computer scientists and don't go too deep into mathematic papers.
Ah sorry, I had no idea, you could have been a topologist who doesn't like computers or something.
LIN is unusual to hear about, probably because it's pretty well understood. Are you more of a coder, or an actual, academic computer scientist? If the latter, what do you know about pebbling games on nondeterministic machines?
Oh no worries, I think I stumbled on this in a computer science crosspost.
While I do lean a bit in the academics, my area is mostly in ML / AI so not well read in pebbling games (although it sounds interesting).
Yeah, the first paper I read was pretty heavily reliant on them. As far as I can tell they're laying the pebbles on the execution tree of a nondeterministic machine and then proving something with that.