On the complexity of minimum maximal acyclic matchings

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Combinatorial Optimization Pub Date : 2024-08-07 DOI:10.1007/s10878-024-01200-3
Juhi Chaudhary, Sounaka Mishra, B. S. Panda
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Abstract

Low-Acy-Matching asks to find a maximal matching M in a given graph G of minimum cardinality such that the set of M-saturated vertices induces an acyclic subgraph in G. The decision version of Low-Acy-Matching is known to be \({\textsf{NP}}\)-complete. In this paper, we strengthen this result by proving that the decision version of Low-Acy-Matching remains \({\textsf{NP}}\)-complete for bipartite graphs with maximum degree 6 and planar perfect elimination bipartite graphs. We also show the hardness difference between Low-Acy-Matching and Max-Acy-Matching. Furthermore, we prove that, even for bipartite graphs, Low-Acy-Matching cannot be approximated within a ratio of \(n^{1-\epsilon }\) for any \(\epsilon >0\) unless \({\textsf{P}}={\textsf{NP}}\). Finally, we establish that Low-Acy-Matching exhibits \(\textsf{APX}\)-hardness when restricted to 4-regular graphs.

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论最小最大非循环匹配的复杂性
Low-Acy-Matching要求在给定图G中找到一个最大匹配M,该匹配M的最小卡数使得M饱和顶点集在G中诱导出一个无环子图。已知Low-Acy-Matching的决策版本是\({\textsf{NP}}\)-完全的。在本文中,我们通过证明对于最大阶数为 6 的双峰图和平面完全消元双峰图,Low-Acy-Matching 的决策版本仍然是 \({\textsf{NP}}\) -complete 的,从而加强了这一结果。我们还展示了 Low-Acy-Matching 和 Max-Acy-Matching 之间的难度差异。此外,我们还证明了,即使对于双峰图,对于任意 \(\epsilon >0\),Low-Acy-Matching 也不能在 \(n^{1-\epsilon }\) 的比率内被近似,除非 \({\textsf{P}}={textsf{NP}}\).最后,我们证明了当限制在 4-regular graphs 时,Low-Acy-Matching 展示了 \(\textsf{APX}\)-hardness。
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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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