网格识别:经典和参数化计算视角

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2023-09-01 DOI:10.1016/j.jcss.2023.02.008
Siddharth Gupta , Guy Sa'ar , Meirav Zehavi
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引用次数: 0

摘要

在过去的几十年里,大量的工作研究了网格图上各种计算问题的可处理性,这些问题通常比一般图产生更快的算法。不幸的是,网格图的识别是困难的——1987年就已经证明它是NP困难的。在本文中,我们在参数化复杂性的框架下提供了这方面的几个积极结果。具体而言,我们的贡献有三方面。首先,我们证明了问题是由k+mcc参数化的FPT,其中mcc是G的连通分量的最大大小。其次,我们提出了一个新的参数化,表示为aG,将图距离与几何距离联系起来。我们证明了该问题是由aG参数化的准NP困难问题,但在树上由aG进行参数化的FPT,以及由k+aG进行的FPT。第三,我们证明了在路径宽度为2的图上,当k=3时,k×r网格图的识别是NP困难的。
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Grid recognition: Classical and parameterized computational perspectives

Over the past few decades, a large body of works studied the (in)tractability of various computational problems on grid graphs, which often yield substantially faster algorithms than general graphs. Unfortunately, the recognition of a grid graph is hard—it was shown to be NP-hard already in 1987. In this paper, we provide several positive results in this regard in the framework of parameterized complexity. Specifically, our contribution is threefold. First, we show that the problem is FPT parameterized by k+mcc where mcc is the maximum size of a connected component of G. Second, we present a new parameterization, denoted aG, relating graph distance to geometric distance. We show that the problem is para-NP-hard parameterized by aG, but FPT parameterized by aG on trees, as well as FPT parameterized by k+aG. Third, we show that the recognition of k×r grid graphs is NP-hard on graphs of pathwidth 2 where k=3.

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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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