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arXiv 1902.11006cs.CC
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一类多项式核

A Hierarchy of Polynomial Kernels

Jouke Witteveen, Ralph Bottesch, Leen Torenvliet

摘要:

在参数化算法中,核化过程被定义为一个多项式时间算法,它将给定问题的实例转换为一个大小受参数函数限制的等价实例。由于之后可以解决这个较小的实例以找到原始问题的答案,因此内核化通常被视为预处理的一种形式。核化的一个自然概括是允许产生许多较小的实例来提供原始问题的答案的过程,也可能使用否定。这种泛化称为图灵核化。立即出现的问题是等价性,或者什么时候一种形式可能而另一种形式不可能。这些都是参数化复杂性中长期存在的公开问题。在本文中,我们回答了其中的许多问题。特别是,我们表明,图灵核化不仅不同于常规核化,但也从中间形式的真值表核化。我们实现了绝对结果的对角化,也取决于广泛接受的复杂性理论假设的自然问题的结果。特别是,我们改进了已知的下界的内核大小的组合问题,使用这些假设。

英文摘要:

In parameterized algorithmics, the process of kernelization is defined as a polynomial time algorithm that transforms the instance of a given problem to an equivalent instance of a size that is limited by a function of the parameter. As, afterwards, this smaller instance can then be solved to find an answer to the original question, kernelization is often presented as a form of preprocessing. A natural generalization of kernelization is the process that allows for a number of smaller instances to be produced to provide an answer to the original problem, possibly also using negation. This generalization is called Turing kernelization. Immediately, questions of equivalence occur or, when is one form possible and not the other. These have been long standing open problems in parameterized complexity. In the present paper, we answer many of these. In particular, we show that Turing kernelizations differ not only from regular kernelization, but also from intermediate forms as truth-table kernelizations. We achieve absolute results by diagonalizations and also results on natural problems depending on widely accepted complexity theoretic assumptions. In particular, we improve on known lower bounds for the kernel size of compositional problems using these assumptions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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