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Data Reduction for Maximum Matching on Real-World Graphs: Theory and Experiments

André Nichterlein (TU Berlin)

 

Finding a maximum-cardinality or maximum-weight matching in (edge-weighted) undirected graphs is among the most prominent problems of algorithmic graph theory. For n-vertex and m-edge graphs, the best known algorithms run in O(m√n) time. We build on recent theoretical work focusing on linear-time data reduction rules for finding maximum-cardinality matchings and complement the theoretical results by presenting and analyzing (thereby employing the kernelization methodology of parameterized complexity analysis) linear-time data reduction rules for the positive-integer-weighted case. Moreover, we experimentally demonstrate that these data reduction rules provide significant speedups of the state-of-the art implementation for computing matchings in real-world graphs: the average speedup is 3800% in the unweighted case and "just" 30% in the weighted case.

This is joint work with Viatcheslav Korenwein, Philipp Zschoche, and Rolf Niedermeier.

 

 

Date
Speaker
Location
Language
13.12.2018
16:15
André Nichterlein
TEL 512
English

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