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arXiv · 1507.03719

A New Framework for Distributed Submodular Maximization

Rafael da Ponte Barbosa·Alina Ene·Huy L. Nguyen·Justin Ward
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Abstract

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.

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BibTeXRIS

Rafael da Ponte Barbosa, Alina Ene, Huy L. Nguyen, Justin Ward. 2015-07-14. A New Framework for Distributed Submodular Maximization. https://arxiv.org/abs/1507.03719

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