Integer & Nubus
Nubus
Have you ever tried to formalize the classic proof that the halting problem is undecidable, and then thought about whether there are any useful heuristics we can build on top of that? I’m curious to see how you’d structure the reduction from a Turing machine to a decision problem, and then maybe we can brainstorm ways to approximate it in practice.
Integer
Yeah, I can sketch it quickly. You take a Turing machine M and encode it as a string ⟨M⟩, then define a new machine H that on input ⟨M⟩, ⟨w⟩ simulates M on w. If M halts, H halts; otherwise it runs forever. The halting problem reduces to deciding whether H halts on ⟨M⟩,⟨w⟩, which is impossible. For a decision problem, you can formalize it as a language L = {⟨M⟩,⟨w⟩ | M halts on w}. The reduction is just the mapping f(⟨M⟩,⟨w⟩)=⟨M⟩,⟨w⟩ itself.
As for heuristics, you can’t prove halting, but you can approximate. Timeouts are the simplest: run M for a fixed number of steps, if it’s still running, guess “non‑halting.” Static analysis tools try to find loops with no exit conditions, or detect recursive calls without a base case. Machine learning approaches can learn patterns from known halting/non‑halting examples, but they’re only probabilistic. The key is that any heuristic will have false positives or negatives, so you usually combine multiple signals: step limits, pattern matching, and maybe a small proof search for a termination proof. It’s never perfect, but it can be useful for practical programs.
Nubus
That outline hits the usual spots, but I keep wondering whether we’re really capturing the subtlety of the reduction. If we encode M as ⟨M⟩, that string already contains enough information to simulate the whole machine, but we still need to be careful about the encoding scheme so that the mapping is computable and doesn’t blow up the input size. When you say “f(⟨M⟩,⟨w⟩)=⟨M⟩,⟨w⟩ itself,” that’s fine, but in practice we’d want a clean separator or a fixed‑length header so that a parser can reliably split the pair. On the heuristic side, timeouts feel like the blunt instrument we all default to, but I’m intrigued by the idea of a hybrid approach that pairs a timeout with a lightweight static analyzer that can flag simple infinite loops before we even hit the step limit. And I keep thinking about how a small proof search, maybe using a simple induction schema, could sometimes catch a pattern that a timeout would miss. It’s all about layering signals so we reduce both false positives and negatives, even if we can’t eliminate them entirely.
Integer
You’re right about the encoding detail – a delimiter or length prefix keeps the reduction clean and guarantees a polynomial‑time translator. A lightweight static analyzer that looks for obvious infinite loops is a good first layer, then a timeout for the rest. A small proof engine that can apply induction on bounded counters or loop invariants can catch those edge cases a timeout would miss. Layering those signals turns the heuristic into a cascade: quick static checks, then bounded simulation, finally a quick proof search if needed. It won’t be perfect, but it trims the false positives that pure timeouts generate.
Nubus
Sounds like a solid stack. I’d also add a quick pattern‑matching pass for known recursion‑no‑base cases – those are cheap to detect and often the biggest source of false negatives. Once the layers are tuned, you can benchmark against a curated set of borderline halting/non‑halting programs to see where each layer wins or fails. Maybe try to formalize the cascade as a finite state machine so you can reason about its completeness and complexity. What do you think?
Integer
That’s the idea – a quick pattern check, then static analysis, a bounded run, maybe a tiny proof engine. Formalising the cascade as a finite‑state machine is neat: each state corresponds to a layer, you move to the next only if the previous fails. It lets you prove that the whole process terminates and gives a worst‑case cost. Then you can benchmark on a set of borderline cases and see which state is the real bottleneck. I’ll sketch the FSM and run some experiments.
Nubus
Sounds good, just keep the states tight and the transitions simple – you’ll be surprised how much you can squeeze out of a small FSM if you let each layer do just its part. Good luck with the sketch and the experiments.