删除分散的图类I -图类数量有限的情况

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE Journal of Computer and System Sciences Pub Date : 2023-06-15 DOI:10.1016/j.jcss.2023.05.005
Ashwin Jacob , Jari J.H. de Kroon , Diptapriyo Majumdar , Venkatesh Raman
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引用次数: 0

摘要

图删除问题涉及删除少量顶点,以便生成的图属于给定的遗传图类。我们开始研究分散图类的删除问题的自然变化。我们希望删除最多k个顶点,这样得到的图的每个连接组件都属于恒定数量的图类中的一个。作为我们的主要结果,我们证明了当已知与每个恒定数量的图类对应的删除问题是FPT时,这个问题是非一致固定参数可处理的(FPT),并且图属于这些类的性质在计数单调二阶(CMSO)逻辑中是可表达的。虽然这是用参数化复杂度中的一些黑盒定理来证明的,但当每个图类都有一个有限的禁止集时,我们给出了一个更快的FPT算法。
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Deletion to scattered graph classes I - Case of finite number of graph classes

Graph-deletion problems involve deleting a small number of vertices so that the resulting graph belong to a given hereditary graph class. We initiate a study of a natural variation of the problem of deletion to scattered graph classes. We want to delete at most k vertices so that each connected component of the resulting graph belongs to one of the constant number of graph classes. As our main result, we show that this problem is non-uniformly fixed-parameter tractable (FPT) when the deletion problem corresponding to each of the constant number of graph classes is known to be FPT and the properties that a graph belongs to these classes are expressible in Counting Monodic Second Order (CMSO) logic. While this is shown using some black box theorems in parameterized complexity, we give a faster FPT algorithm when each of the graph classes has a finite forbidden set.

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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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