The other day, a student asked me whether, if $P \ne NP$, whether any language outside of $NP$ is known to be $NP$-hard. I wasn't sure if
- This is definitely known to be true,
- This is definitely known to be false, or
- This depends on another set of complexity assumptions that do not immediately follow from $P \ne NP$ (that is, even if we knew $P \ne NP$, this would still be an open question)
None of the texts on complexity I looked into seemed to answer this question (though it is quite possible that I simply missed it). Does anyone know which of the above three is true, or know a good reference where I could look up the answer?
(Note: This earlier question is related, but I'm considering solely questions outside of $NP$ (so the existing answer doesn't really help) and am not restricting this to just the decidable languages)
Thanks!
It seems that the probabilistic method works.
Consider $\{0,1\} \times \{0,1\} \times \dots$ as a probability space, corresponding to throwing a coin countably many times (product measure).
Any event $a \in \{0,1\} \times \{0,1\} \times \dots$ encodes a subset $L \subseteq \{0,1\}^{\ast}$, the consecutive bits decide if $\epsilon \in L, 0 \in L, 1 \in L, 00 \in L, \dots$. We will now select random $L$, equivalently random $a$, and check its properties.
With probability 1, $L \notin \mathsf{NP}$ (since $\mathsf{NP}$ is countable), even more: with probability 1, $L$ is undecidable.
Now, suppose you have a reduction $f \colon A^{\ast} \to \{0,1\}^{\ast}$ that attempts to reduce SAT to $L$. Since $\mathsf{P} \neq \mathsf{NP}$ by assumption, the image of $f$ must be infinite; otherwise you could convert the reduction to a decision procedure for $SAT$. However, for any $x$, it must hold $x \in SAT \iff f(x)\in L$, and that happens with probability 1/2. Since there are infinitely many values for $f(x)$, the probability that $f$ is a valid reduction from SAT to $L$ is 0.
Since there are countably many reductions, and countable intersection of sets of measure 1 has measure 1, the overall probability of $L$ satisfying all conditions is 1.
So a "generic" random language is neither decidable nor $\mathsf{NP}$-hard, unless $\mathsf{P} = \mathsf{NP}$.
It is possible to convert this proof to diagonalization: on $2i$-th stage, you diagonalize against $i$-th $\mathsf{NP}$ problem; on $2i+1$-th stage, you diagonalize against $i$-th polynomial time reduction with infinite image. This gives a constructive example; with more careful bookkeeping you can get a decidable example.