The question (Prove L is NP-hard) was about proving that the following language is NP-hard: $$ L = \{ \langle D_1, D_2, ... ,D_K \rangle : k \in {N}\text{, the } D_i \text{ are DFAs and } {\bigcap}_{i=1}^k L(D_i) = \emptyset \} $$

That got me thinking about the related problem:

$$ L' = \{ \langle D_1, D_2, ... ,D_K \rangle : k \in {N}\text{, the } D_i \text{ are DFAs and } {\bigcap}_{i=1}^k L(D_i) \neq \emptyset \} $$

I would imagine that $L'$ is also NP-hard, but I couldn't think of any reductions.. am I missing something obvious?


1 Answer 1


The language $L$ is in fact PSPACE-complete, so in particular it's both NP-hard and coNP-hard. Here is a quick sketch of the proof.

$L$ is in PSPACE. This part is easy. The intersection of the DFAs can be realized as a DFA with $n^n$ many states, hence if the intersection is non-empty, there must be a word of length $n^n$ accepted by all DFAs. Counting up to $n^n$ requires $O(n\log n)$ space, so there is an NPSPACE machine for $L$: the machine guesses a word of length at most $n^n$ and verifies (in parallel) that the word is accepted by all DFAs. Savitch's theorem shows that NPSPACE=PSPACE, so we're done.

$L$ is PSPACE-hard. The reduction is from TQBF, which is the problem, given a formula $\varphi(x_1,\ldots,x_n)$, to decide the truth value of $\psi = \exists x_1 \forall x_2 \exists x_3 \cdots \varphi(x_1,\ldots,x_n)$. Consider some iterative algorithm for evaluating $\psi$: it goes over all possible assignments of $x_1,\ldots,x_n$, and computes intermediate values of $Q x_i \bar{Q} x_{i+1} \cdots \varphi(y_1,\ldots,y_{i-1},x_i,\ldots,x_n)$ (where $Q \in \{\forall,\exists\}$). We can write this computation as one long string $w$. The computation can be verified locally: for example, if all we wanted is to verify that $w$ is a $\#$-separated list of all numbers from $0$ to $2^n-1$ in binary (i.e. for $n=2$, $\#00\#01\#10\#11$), then we can write several regular expressions which verify this:

  • $w$ is a $\#$-separated list: $(\#(0+1)^n)^*$
  • $w$ starts with $0^n$: $\#0^n\Sigma^*$
  • $w$ ends with $1^n$: $\Sigma^*\#1^n$
  • The LSBs in $w$ behaves correctly: $(\#(0+1)^{n-1}0\#(0+1)^{n-1}1)^*$
  • The MSBs in $w$ behaves correctly: $(\sum_{i=0}^{n-2} \#0(0+1)^i0(0+1)^{n-2-i})^*\#01^{n-1}\#10^{n-1}(\#1(0+1)^{n-1})^*$
  • Similar (but more complicated) regular expression ensure that the other bits behave correctly.

All these expressions (and the corresponding DFAs!) are of length polynomial in $n$, and there is a polynomial number of them, so using DFA intersection you can express the fact that $w$ is of the given form.

The actual $w$ is more complicated, but the idea is the same. At the very end of $w$ will appear the actual value of $\psi$, and we can add a regular expression which verifies that $\psi$ is true. This reduces TQBF to DFA intersection.

The classical reference is Kozen's Lower bounds for natural proof systems, or you can check my account (Section 4) which proves a different result but contains everything that you need to prove that $L$ is PSPACE-hard.

  • $\begingroup$ Thank you very much for the very complete answer. Your paper in particular is extremely helpful. I will certainly be sharing with classmates. $\endgroup$
    – Kevin G
    Commented Nov 27, 2013 at 5:47
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    $\begingroup$ This is fascinating. Can you elaborate on how you write this computation as one long string $w$? That part wasn't self-evident to me. How is the string $w$ defined? $\endgroup$
    – D.W.
    Commented Nov 30, 2013 at 6:59
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    $\begingroup$ The string $w$ contains $\#$-separated sequence of snapshots (so to speak) of the work tape of a Turing machine computing TQBF. The snapshots only differ locally, which facilitates verifying this fact using DFA intersection. A similar situation occurs in Cook's theorem. $\endgroup$ Commented Dec 1, 2013 at 1:23

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