169,331 research outputs found

    Distance Constraint Satisfaction Problems

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    We study the complexity of constraint satisfaction problems for templates Γ\Gamma that are first-order definable in (Z;succ)(\Bbb Z; succ), the integers with the successor relation. Assuming a widely believed conjecture from finite domain constraint satisfaction (we require the tractability conjecture by Bulatov, Jeavons and Krokhin in the special case of transitive finite templates), we provide a full classification for the case that Gamma is locally finite (i.e., the Gaifman graph of Γ\Gamma has finite degree). We show that one of the following is true: The structure Gamma is homomorphically equivalent to a structure with a d-modular maximum or minimum polymorphism and CSP(Γ)\mathrm{CSP}(\Gamma) can be solved in polynomial time, or Γ\Gamma is homomorphically equivalent to a finite transitive structure, or CSP(Γ)\mathrm{CSP}(\Gamma) is NP-complete.Comment: 35 pages, 2 figure

    Random Constraint Satisfaction Problems

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    Random instances of constraint satisfaction problems such as k-SAT provide challenging benchmarks. If there are m constraints over n variables there is typically a large range of densities r=m/n where solutions are known to exist with probability close to one due to non-constructive arguments. However, no algorithms are known to find solutions efficiently with a non-vanishing probability at even much lower densities. This fact appears to be related to a phase transition in the set of all solutions. The goal of this extended abstract is to provide a perspective on this phenomenon, and on the computational challenge that it poses

    Robustly Solvable Constraint Satisfaction Problems

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    An algorithm for a constraint satisfaction problem is called robust if it outputs an assignment satisfying at least (1−g(ε))(1-g(\varepsilon))-fraction of the constraints given a (1−ε)(1-\varepsilon)-satisfiable instance, where g(ε)→0g(\varepsilon) \rightarrow 0 as ε→0\varepsilon \rightarrow 0. Guruswami and Zhou conjectured a characterization of constraint languages for which the corresponding constraint satisfaction problem admits an efficient robust algorithm. This paper confirms their conjecture

    Automated formulation of constraint satisfaction problems

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