2017年12月
Decomposition theorems for square-free 2-matchings in bipartite graphs
DISCRETE APPLIED MATHEMATICS
- 巻
- 233
- 号
- 開始ページ
- 215
- 終了ページ
- 223
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1016/j.dam.2017.07.035
- 出版者・発行元
- ELSEVIER SCIENCE BV
A C-k-free 2-matching in an undirected graph is a simple 2-matching which does not contain cycles of length k or less. The complexity of finding the maximum C-k-free 2-matching in a given graph varies depending on k and the type of input graph. The case where k = 4 and the graph is bipartite, which is called the maximum square-free 2-matching problem in bipartite graphs, is well-solved. Previous results for this special case include min-max theorems, polynomial combinatorial algorithms, a linear programming formulation with dual integrality for a weighted version, and discrete convex structures.
In this paper, we further investigate the structure of square-free 2-matchings in bipartite graphs and present new decomposition theorems. These theorems serve as analogues of the Dulmage-Mendelsohn decomposition for matchings in bipartite graphs and the Edmonds-Gallai decomposition for matchings in nonbipartite graphs. We exhibit two canonical minimizers for the set function in the min-max formula, and a characterization of the maximum square-free 2-matchings with the aid of these canonical minimizers. (C) 2017 Elsevier B.V. All rights reserved.
In this paper, we further investigate the structure of square-free 2-matchings in bipartite graphs and present new decomposition theorems. These theorems serve as analogues of the Dulmage-Mendelsohn decomposition for matchings in bipartite graphs and the Edmonds-Gallai decomposition for matchings in nonbipartite graphs. We exhibit two canonical minimizers for the set function in the min-max formula, and a characterization of the maximum square-free 2-matchings with the aid of these canonical minimizers. (C) 2017 Elsevier B.V. All rights reserved.
- リンク情報
- ID情報
-
- DOI : 10.1016/j.dam.2017.07.035
- ISSN : 0166-218X
- eISSN : 1872-6771
- Web of Science ID : WOS:000413613800019