Solving MAX-r-SAT Above a Tight Lower Bound
Noga Alon, Gregory Gutin, Eun Jung Kim, Stefan Szeider, and Anders Yeo

TL;DR
This paper introduces an exact fixed-parameter algorithm for Max-r-SAT that operates efficiently above a tight lower bound, utilizing polynomial reductions and probabilistic methods, and establishes polynomial kernels for the problem.
Contribution
It presents the first fixed-parameter tractable algorithm for Max-r-SAT above the lower bound, introduces bikernelization, and extends results to broader Boolean CSPs.
Findings
Algorithm runs in O(m) + 2^{O(k^2)} time for fixed r
Max-r-SAT admits a polynomial-size kernel
Enhanced techniques for Boolean CSPs
Abstract
We present an exact algorithm that decides, for every fixed in time whether a given multiset of clauses of size admits a truth assignment that satisfies at least clauses. Thus \textsc{Max--Sat} is fixed-parameter tractable when parameterized by the number of satisfied clauses above the tight lower bound . This solves an open problem of Mahajan et al. (J. Comput. System Sci., 75, 2009). Our algorithm is based on a polynomial-time data reduction procedure that reduces a problem instance to an equivalent algebraically represented problem with variables. This is done by representing the instance as an appropriate polynomial, and by applying a probabilistic argument combined with some simple tools from Harmonic analysis to show that if the polynomial cannot be reduced to one of size , then there is a…
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Taxonomy
TopicsFormal Methods in Verification · Constraint Satisfaction and Optimization · Model-Driven Software Engineering Techniques
