Parameterized Complexity of MaxSat Above Average
Robert Crowston, Gregory Gutin, Mark Jones, Venkatesh Raman, and Saket, Saurabh

TL;DR
This paper investigates the parameterized complexity of MaxSat above average, proving para-NP-completeness for certain parameter ranges and establishing bounds related to the clause size function r(n).
Contribution
It demonstrates that MaxSat-AA is para-NP-complete and provides complexity bounds for Max-$r(n)$-Sat-AA based on the growth of r(n), contrasting with known fixed-parameter tractability results.
Findings
MaxSat-AA is para-NP-complete, unlike MaxLin2-AA.
Max-$r(n)$-Sat-AA is para-NP-complete for r(n)=⎡log n⎤.
Under ETH, Max-$r(n)$-Sat-AA is not in XP for r(n)≥log log n + φ(n).
Abstract
In MaxSat, we are given a CNF formula with variables and clauses and asked to find a truth assignment satisfying the maximum number of clauses. Let be the number of literals in the clauses of . Then is the expected number of clauses satisfied by a random truth assignment (the truth values to the variables are distributed uniformly and independently). It is well-known that, in polynomial time, one can find a truth assignment satisfying at least clauses. In the parameterized problem MaxSat-AA, we are to decide whether there is a truth assignment satisfying at least clauses, where is the parameter. We prove that MaxSat-AA is para-NP-complete and, thus, MaxSat-AA is not fixed-parameter tractable unless PNP. This is in sharp contrast to MaxLin2-AA which was recently proved to be fixed-parameter…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Optimization and Search Problems · Machine Learning and Algorithms
