Ttdfjt

Below is my simplification of part of a larger research project on spatial Bayesian networks:

Say a variable is "$k$-local" in a string $C in 3text-CNF$ if there are fewer than $k$ clauses between the first and last clause in which it appears (where $k$ is a natural number).

Now consider the subset $(3,k)text-LSAT subseteq 3text-SAT$ defined by the criterion that for any $C in (3,k)text-LSAT$, every variable in $C$ is $k$-local. For what $k$ (if any) is $(3,k)text-LSAT$ NP-hard?

Here is what I have considered so far:

(1) Variations on the method of showing that $2text-SAT$ is in P by rewriting each disjunction as an implication and examining directed paths on the directed graph of these implications (noted here and presented in detail on pp. 184-185 of Papadimitriou's Computational Complexity). Unlike in $2text-SAT$, there is branching of the directed paths in $(3,k)text-LSAT$, but perhaps the number of directed paths is limited by the spatial constraints on the variables. No success with this so far though.

(2) A polynomial-time reduction of $3text-SAT$ (or other known NP-complete problem) to $(3,k)text-LSAT$. For example, I've tried various schemes of introducing new variables. However, bringing together the clauses that contain the original variable $x_k$ generally requires that I drag around "chains" of additional clauses containing the new variables and these interfere with the spatial constraints on the other variables.

Surely I'm not in new territory here. Is there a known NP-hard problem that can be reduced to $(3,k)text-LSAT$ or do the spatial constraints prevent the problem from being that difficult?

$(3,k)text-LSAT$ is in P for all $k$. As you have indicated, locality is a big obstruction to NP-completeness.

Here is a polynomial algorithm.

Input: $phiin (3,k)text-LSAT$, $phi=c_1wedge c_2cdots wedge c_m$, where $c_i$ is the $i$-th clause.

Output: true if $phi$ becomes 1 under some assignment of all variables.

Procedure:

1. Construct set $B_i$, the variables that appear in at least one of $c_i, c_i+1, cdots, c_i+k$, $1le ile m-k$.


2. Construct set $A_i=f: B_ito0,1 mid c_i, c_i+1, cdots, c_i+k text become 1 under f$.


3. Construct set $E=cup_i(f, g)mid fin A_i, gin A_i+1, f(x)=g(x)text for all xin B_icap B_i+1 $


4. Let $V=A_1cup A_2cdotscup A_m-k$. Consider directed graph $G(V,E)$. For each vertex in $A_1$, start a depth-first search on $G$ to see if we can reach a vertex in $A_m-k$. If found, return true.


5. If we have reached here, return false.

The correctness of the algorithm above comes from the following claim.

Claim. $phi$ is satisfiable $Longleftrightarrow$ there is a path in $G$ from a vertex in $A_1$ to a vertex in $A_m-k$.
Proof.

"$Longrightarrow$": Suppose $phi$ becomes 1 under assignment $f$. Let $f_i$ be the restriction of $f$ to $B_i$. Then we have a path $f_1, cdots, f_m-k$.

"$Longleftarrow$": Suppose there is a path $f_1, cdots, f_m-k$, where $f_1in A_1$ and $f_m-kin A_m-k$. Define assignment $f$ such that $f$ agrees with all $f_i$, i.e., $f(x)=f_i(x)$ if $xin B_i$. We can verify that $f$ is well-defined. Since $c_ell$ becomes 1 for some $f_j$ for all $ell$, $phi$ becomes 1 under $f$.

The number of vertices $|V|le 2^3(k+1)(m-k)$. Hence the algorithm runs in polynomial time in term of $m$, the number of clauses and $n$, the number of total variables.