文章基本信息

标题：The Power of Linear-Time Data Reduction for Maximum Matching
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作者：George B. Mertzios ; Andr{\'e} Nichterlein ; Rolf Niedermeier 等
期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
电子版ISSN：1868-8969
出版年度：2017
卷号：83
页码：46:1-46:14
DOI：10.4230/LIPIcs.MFCS.2017.46
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：Finding maximum-cardinality matchings in undirected graphs is arguably one of the most central graph primitives. For m-edge and n-vertex graphs, it is well-known to be solvable in O(m\sqrt{n}) time; however, for several applications this running time is still too slow. We investigate how linear-time (and almost linear-time) data reduction (used as preprocessing) can alleviate the situation. More specifically, we focus on linear-time kernelization. We start a deeper and systematic study both for general graphs and for bipartite graphs. Our data reduction algorithms easily comply (in form of preprocessing) with every solution strategy (exact, approximate, heuristic), thus making them attractive in various settings.
关键词：Maximum-cardinality matching; bipartite graphs; linear-time algorithms; kernelization; parameterized complexity analysis; FPT in P