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Oral
Distributed Weighted Matching via Randomized Composable Coresets
Sepehr Assadi · Mohammad Hossein Bateni · Vahab Mirrokni

Wed Jun 12 02:00 PM -- 02:20 PM (PDT) @ Room 102
in Combinatorial Optimization »

Maximum weight matching is one of the most fundamental combinatorial optimization problems with a wide range of applications in data mining and bioinformatics. Developing distributed weighted matching algorithms has been challenging due to the sequential nature of efficient algorithms for this problem. In this paper, we develop a simple distributed algorithm for the problem on general graphs with approximation guarantee of 2 + eps that (nearly) matches that of the sequential greedy algorithm. A key advantage of this algorithm is that it can be easily implemented in only two rounds of computation in modern parallel computation frameworks such as MapReduce. We also demonstrate the efficiency of our algorithm in practice on various graphs (some with half a trillion edges) by achieving objective values always close to what is achievable in the centralized setting.

[ Slides] 

Author Information

Sepehr Assadi (Princeton University)
Mohammad Hossein Bateni (Google Research)
Vahab Mirrokni (Google Research)

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