The Curse and Blessing of Not-All-Equal in k-Satisfiability
S. Cliff Liu

TL;DR
This paper introduces faster algorithms for NAE-$k$-SAT and MAX-NAE-$k$-SAT, showing that NAE-$k$-SAT can be easier than $k$-SAT exactly, but approximate solutions for MAX-NAE-$k$-SAT may be harder for larger $k$.
Contribution
It presents the first algorithms that outperform $k$-SAT for NAE-$k$-SAT and provides new bounds for approximating MAX-NAE-$k$-SAT, highlighting differences in complexity between NAE-$k$-SAT and $k$-SAT.
Findings
NAE-$k$-SAT can be solved faster than $k$-SAT for all $k \\ge 3$.
New upper bounds for MAX-NAE-$k$-SAT approximation algorithms.
Approximate MAX-NAE-$k$-SAT is harder than MAX-$k$-SAT for $k \\ge 4$.
Abstract
As a natural variant of the -SAT problem, NAE--SAT additionally requires the literals in each clause to take not-all-equal (NAE) truth values. In this paper, we study the worst-case time complexities of solving NAE--SAT and MAX-NAE--SAT approximation, as functions of , the number of variables , and the performance ratio . The latter problem asks for a solution of at least times the optimal. Our main results include: (1) A deterministic algorithm for NAE--SAT that is faster than the best deterministic algorithm for -SAT on all . Previously, no NAE--SAT algorithm is known to be faster than -SAT algorithms. For , we achieve an upper bound of . The corresponding bound for -SAT is . (2) A randomized algorithm for MAX-NAE--SAT approximation, with upper bound where…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Advanced Graph Theory Research · Optimization and Search Problems
