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dc.contributor.authorEne, Alinaen_US
dc.contributor.authorNguyen, Huy Leen_US
dc.date.accessioned2021-09-20T19:02:40Z
dc.date.available2021-09-20T19:02:40Z
dc.date.issued2020-08-14
dc.identifier.citationAlina Ene, Huy Le Nguyen. 2020. "Parallel Algorithm for Non-Monotone DR-Submodular Maximization." International Conference on Machine Learning
dc.identifier.urihttps://hdl.handle.net/2144/43038
dc.description.abstractIn this work, we give a new parallel algorithm for the problem of maximizing a non-monotone diminishing returns submodular function subject to a cardinality constraint. For any desired accuracy 𝜖, our algorithm achieves a 1/e − 𝜖 approximation using O(log n log(1/𝜖 )/𝜖^3) parallel rounds of function evaluations. The approximation guarantee nearly matches the best approximation guarantee known for the problem in the sequential setting and the number of parallel rounds is nearly-optimal for any constant 𝜖. Previous algorithms achieve worse approximation guarantees using Ω (log^2 n) parallel rounds. Our experimental evaluation suggests that our algorithm obtains solutions whose objective value nearly matches the value obtained by the state of the art sequential algorithms, and it outperforms previous parallel algorithms in number of parallel rounds, iterations, and solution quality.en_US
dc.language.isoen_US
dc.rightsCopyright 2020 by the author(s).en_US
dc.titleParallel algorithm for non-monotone DR-submodular maximizationen_US
dc.typeConference materialsen_US
dc.description.versionPublished versionen_US
pubs.elements-sourcemanual-entryen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.identifier.mycv615577


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