The Halting Problem is a terrible example of NP-Harder

The example given doesn't seem right to me.

> There is one problem, though, that I find easily explainable. Place a token at the bottom left corner of a grid that extends infinitely up and right, call that point (0, 0). You're given list of valid displacement moves for the token, like (+1, +0), (-20, +13), (-5, -6), etc, and a target point like (700, 1). You may make any sequence of moves in any order, as long as no move ever puts the token off the grid. Does any sequence of moves bring you to the target?

If someone gives you such a sequence, it seems trivial to verify it in linear time. Even for arbitrary dimensions, and such witness can be verified in linear time.

To be in NP, witness must be verifiable in polynomial time with respect to the size of the original input. In this problem (VAS Reachability), the solution can be `2^2^2^...^K` steps long. Even if that's linear with respect to the witness, it's not polynomial with respect to the set of moves + goal.

Hmm.. I'd love to see a more formal statement of this, because it feels unintuitive.

Notably the question "given a number as input, output as many 1's as that number" is exponential in the input size. Is this problem therefore also strictly NP-hard?

> Hmm.. I'd love to see a more formal statement of this, because it feels unintuitive.

The problem is called "Vector Addition System Reachability", and the proof that it's Ackermann-complete is here: https://arxiv.org/pdf/2104.13866 (It's actually for "Vector Addition Systems with states, but the two are equivalent formalisms. They're also equivalent to Petri nets, which is what got me interested in this problem in the first place!)

> Notably the question "given a number as input, output as many 1's as that number" is exponential in the input size. Is this problem therefore also strictly NP-hard?

(Speaking off the cuff, so there's probably a mistake or two here, computability is hard and subtle!)

Compression features in a lot of NP-hard problems! For example, it's possible to represent some graphs as "boolean circuits", eg a function `f(a, b)` that's true iff nodes `a,b` have an edge between them. This can use exponentially less space, which means a normal NP-complete problem on the graph becomes NEXPTIME-complete if the graph is encoded as a circuit.

IMO this is cheating, which is why I don't like it as an example.

"Given K as input, output K 1's" is not a decision problem because it's not a boolean "yes/no". "Given K as input, does it output K ones" is a decision problem. But IIRC then for `K=3` your input is `(3, 111)` so it's still linear on the input size. I think.