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arXiv 1609.08879cs.DS
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数据简化对匹配的影响

The Power of Data Reduction for Matching

George B. Mertzios, André Nichterlein, Rolf Niedermeier

摘要:

在无向图中寻找最大基数匹配可以说是最核心的图原语之一。对于$m$-边图和$n$-顶点图,众所周知,它可以在$O(m\sqrt{n})$时间内求解;然而,对于一些应用程序,这个运行时间仍然太慢。我们调查如何线性时间(几乎线性时间)数据减少(用作预处理)可以缓解这种情况。更具体地说,我们专注于(几乎)线性时间内核化。本文对一般图和二部图进行了深入系统的研究。我们的数据简化算法很容易遵守(以预处理的形式)每一个解决方案的策略(精确,近似,启发式),从而使他们在各种设置有吸引力。

英文摘要:

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 (almost) 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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