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arXiv 0902.1835cs.CC
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MIN F^+Pi_1和MAX NP的多项式核化

Polynomial Kernelizations for MIN F^+Pi_1 and MAX NP

Stefan Kratsch

摘要:

在许多地方已经观察到,常数因子可近似问题通常允许多项式甚至线性问题内核用于其决策版本,例如,顶点覆盖,反馈顶点集,三角形包装。虽然存在像装箱这样的例子,除非P = NP,否则不允许任何内核,但这两种多项式时间技术之间显然有很强的联系。我们通过证明两类突出的常数因子可逼近问题(即MIN F^+ NP和MAX NP)中所有问题的自然决策版本都允许多项式问题核来补充这一图景。MAX NP的一个子类MAX SNP中的问题被证明允许具有线性基集的核,例如,图的顶点的集合。这扩展了Cai和Chen(JCSS 1997)的结果,指出MAX SNP和MIN F^+ H_1中问题的标准参数化是固定参数可处理的,并补充了最近关于不允许多项式核化的问题的研究(Bodlaender et al. JCSS 2009)。

英文摘要:

It has been observed in many places that constant-factor approximable problems often admit polynomial or even linear problem kernels for their decision versions, e.g., Vertex Cover, Feedback Vertex Set, and Triangle Packing. While there exist examples like Bin Packing, which does not admit any kernel unless P = NP, there apparently is a strong relation between these two polynomial-time techniques. We add to this picture by showing that the natural decision versions of all problems in two prominent classes of constant-factor approximable problems, namely MIN F^+Π_1 and MAX NP, admit polynomial problem kernels. Problems in MAX SNP, a subclass of MAX NP, are shown to admit kernels with a linear base set, e.g., the set of vertices of a graph. This extends results of Cai and Chen (JCSS 1997), stating that the standard parameterizations of problems in MAX SNP and MIN F^+Π_1 are fixed-parameter tractable, and complements recent research on problems that do not admit polynomial kernelizations (Bodlaender et al. JCSS 2009).

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