I don't know who "we" are, but most proofs of algorithm correctness use classical logic.
Also, there's nothing "obvious" about what you said unless you want proof objects, and why you'd want that is far from obvious in itself.
I don't know who "we" are, but most proofs of algorithm correctness use classical logic.
Also, there's nothing "obvious" about what you said unless you want proof objects, and why you'd want that is far from obvious in itself.
I challenge you on "most proofs of algorithm correctness use classical logic". That means double negation elimination, or excluded middle. I bet most proofs don't use those. Give examples.
Because if they benefited from it (in surfacing computational content, which is the whole point of constructive proof) they'd be comprised within the algorithm, not the proof.
The point of a constructive proof is that the proof itself is in some way computational [1], not that the algorithm is. When I wrote formal proofs, I used either TLA+ or Isabelle/HOL, neither of which are constructive. It's easy to describe the notion of "constructive computation" in a non-constructive logic without any additional effort (that's because non-constructive logics are a superset of constructive logics; i.e. they strictly admit more theorems).
[1]: Disregarding computational complexity
True, but this requires using different formal systems for the algorithm and the proof. Isabelle/HOL being non-constructive means you can't fully express proof-carrying code in that single system, without tacking on something else for the "purely computational" added content.
You are right that when using non constructive logics, it's not guaranteed that the proof is executable as a program, but that's not a downside. Having the proof be a program in some sense is interesting, but it's not particularly useful.
This is exactly how TLA+ works: https://pron.github.io/posts/tlaplus_part3
Interestingly, we handle static analysis the same way by using language subsets. The larger chunk is unprovable. So, we just work with what's easy to analyze. Then, wrap it in types or contracts to use it properly.
And plenty of testing for when the specs are wrong.
You might also add designing an algorithm to begin with, or porting it from a less restrictive to a more restrictive model of computation, as kinds of proofs in CS that are closely aligned to what we'd call constructive.
I've been using formal methods for years now and have yet to see where constructive logic makes things easier (I'm not saying it necessarily makes things harder, either).