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Approximation Algorithm for Random MAX-kSAT

  • Yannet Interian
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3542)

Abstract

We provide a rigorous analysis of a greedy approximation algorithm for the maximum random k-SAT (MAX-R-kSAT) problem. The algorithm assigns variables one at a time in a predefined order. A variable is assigned TRUE if it occurs more often positively than negatively; otherwise, it is assigned FALSE. After each variable assignment, problem instance is simplified and a new variable is selected. We show that this algorithm gives a 10/9.5-approximation, improving over the 9/8-approximation given by de la Vega and Karpinski [7]. The new approximation ratio is achieved by using a different algorithm than the one proposed in [7], along with a new upper bound on the maximum number of clauses that can be satisfied in a random k-SAT formula [2].

Keywords

Satisfying Assignment Unit Clause Empty Clause Random Formula Greedy Approximation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yannet Interian
    • 1
  1. 1.Center for Applied MathematicsCornell UniversityIthacaUSA

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