# How many clauses are required for SAT to be NP-hard in CNF formulas?

It is not hard to see that SAT for a CNF formula with $$n$$ variables and a constant number of clauses can be solved in polynomial time. On the other hand, it is not hard to see that a CNF formula with $$n$$ variables and $$O(n)$$ clauses is enough for NP-hardness (consider for example the instances of SAT associated with the natural formula for 3-colorability, applied to planar graphs).

We could define this formally as $$\text{CNFSAT}-f-\text{Clauses}$$, a family of problems parameterized by a function $$f$$, in which instances are formulas in CNF such that if they have $$n$$ variables, then they have at most $$f(n)$$ clauses. Based on this, what I'd like to know is what is the smallest function $$g$$ such that we know there exists $$f \in O(g)$$ such that $$\text{CNFSAT}-f-\text{Clauses}$$ is already NP-hard. We know that g = 1 (constant # of clauses) does not work, and $$g = n$$ (linear number of clauses) works.

What about $$g = \log n$$? Is there a simple algorithm for CNFSAT over formulas that have $$O(\lg \lg n)$$ clauses?

Lower bound. For $$g \le c \cdot \sqrt{\log n}$$ there exists a polynomial-time algorithm. The idea is the following: if some clauses have too many variables, then it should be trivial to select some variable to satisfy this clause, without hurting clauses with few variables. We repeat the following:

Find the clause with the smallest number of variables. Let $$x_1,\ldots,x_k$$ be the variables participating in this clause.

• If $$k > g$$, then the entire formula is satisfiable (we process clauses one by one and select a variable which we didn't select before).
• Otherwise, we remove the clause. We also remove $$x_1,\ldots,x_k$$ from all other clauses.

Now, we have to satisfy the removed clauses. Since there are at most $$g$$ clauses and each of them introduces at most $$g$$ new variables, it means that there are at most $$g^2 = c^2 \cdot \log n$$ variables overall. Therefore, there are at most $$n^{c^2}$$ variable combinations, and we can just use brute-force.

Conditional upper bound. It is almost tight in the following sense. Assume that the lower bound on the SAT with $$n$$ variables and $$\ge c\cdot n$$ clauses (for some $$c$$, e.g. coming from $$3$$-coloring) is $$\alpha^n$$ ($$\alpha \in (1, 2]$$). Note that the same lower bound holds after our transformation (since we can just apply it before any algorithm). Therefore, if there are at least $$\log^{1+\epsilon} n$$ clauses, they can have $$\frac {\log^{1+\epsilon} n} c$$ variables and the lower bound for running time for our problem is

$$\alpha^{\frac {\log^{1+\epsilon} n} c} = n^{\frac {\log^\epsilon n \cdot \log \alpha} {c}},$$

which is super-polynomial.

• Thanks Dmitry! This is a great answer! The second argument is similar to arguments I'd seen around the ETH, but I hadn't seen the first one. Jul 18 '20 at 21:31