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I define a long CNF to contain at least $2^\frac{n}{2}$ clauses, where $n$ is the number of its variables. Let $\text{Long-SAT}=\{\phi: \phi$ is a satisfiable long CNF formula$\}$.

I'd like to know why $\text{Long-SAT} \in P$. First I thought it is $\text{NPC}$ since I can do a polynomial-time reduction from $\text{SAT}$ to $\text{Long-SAT}$, no?

But maybe I can reduce $\text{2-SAT}$ to $\text{Long-SAT}$? How do I do that?

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  • $\begingroup$ @Numerator: Watch out in the last sentence in the question. Reducing an easy problem (2-SAT in this case) to your problem does not mean that your problem is easy. $\endgroup$ – Tsuyoshi Ito Jul 13 '12 at 0:02
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    $\begingroup$ @TsuyoshiIto: We try to keep on top of weird tags. Please feel free to edit the occasional occurrence. If you spot wide-spread (mis)use or meet opposition, please take it to meta (rather than discussing in the comments below a question). $\endgroup$ – Raphael Jul 21 '12 at 19:09
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Unless I'm missing something, it's trivially in P as the length of the formula is exponential in the number of variables. Hence all $2^{n}$ truth assignments can be generated and checked in polynomial time in the length of the formula.

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  • $\begingroup$ But a $2^n$ checks is still defined as plynoimal time? $\endgroup$ – Numerator Jul 12 '12 at 10:09
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    $\begingroup$ Remember that to be in P you want an algorithm that runs in polynomial time in the size of the input. In this case, if we denote the size of the input as N we know that $\text{#clauses } \leq N$. Hence we also have $n=O(log N)$ so the $2^n$ assignments only amount to a polynomial in the overall input size $N$. Don't let texts trick you when they use the variable $n$, it's just a variable, not a special magic number that is always the best measure for the size of the input. Sorry about the formatting, I'm typing this on my phone. $\endgroup$ – Luke Mathieson Jul 12 '12 at 11:37
  • $\begingroup$ @Numerator: you are doing $2^{\log n}=n$ checks, where $n$ is the length of the input. $\endgroup$ – Xodarap Jul 21 '12 at 19:05
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In this case, the answer is trivial as Luke points out. However, as you seem to have come up with the definition yourself, note this.

For SAT, so-called phase transitions regarding the ratio of variable count to clause count have been observed [1,2]. If it is small, instances are easy, and hard if it is large. There seems to be a more or less sharp transition from easy to hard. This seems to be an active area of research. cstheory.SE has some more on this phenomenon.

So, if you adjust your definition of "long" to polynomial blowup, you might indeed get an non-trivially easy class -- that is, in P -- just because you have much more clauses than variables.


  1. The SAT Phase Transition by I. P. Gent (1994)
  2. Determining computational complexity from characteristic 'phase transitions' by R. Monasson, R. Zecchina et al. (1999)
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    $\begingroup$ Actually, it's not an easy-hard but rather easy-hard-easy the pattern regarding the phase transition. There are 2 regions: underconstrained and overconstrained. In the first one, solutions are densely distributed, so you succeed quickly. In the second one you fail quickly: any reasonable algorithm finds a solution if such exists (a strong basin of attraction), and if there's no solution a backtracking algorithm can establish that quickly since potential solution paths are cut off early. Hard problems are on the boundary of these regions: the probability of a solution is low but non-negligible. $\endgroup$ – Juho Jul 21 '12 at 22:45

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