A New Framework for Distributed Submodular Maximization
A wide variety of problems in machine learning, including exemplar clustering, document summarization, and sensor placement, can be cast as constrained submodular maximization problems. A lot of recent effort has been devoted to developing distributed algorithms for these problems. However, these results suffer from high number of rounds, suboptimal approximation ratios, or both. We develop a framework for bringing existing algorithms in the sequential setting to the distributed setting, achieving near optimal approximation ratios for many settings in only a constant number of MapReduce rounds. Our techniques also give a fast sequential algorithm for non-monotone maximization subject to a matroid constraint.
WOS:000391198500068
2016
978-1-5090-3933-3
New York
10
Annual IEEE Symposium on Foundations of Computer Science
645
654
REVIEWED
Event name | Event place | Event date |
New Brunswick, NJ | OCT 09-11, 2016 | |