An optimal approximation for submodular maximization under a matroid constraint in the adaptive complexity model

@article{Balkanski2019AnOA,
  title={An optimal approximation for submodular maximization under a matroid constraint in the adaptive complexity model},
  author={Eric Balkanski and A. Rubinstein and Y. Singer},
  journal={Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing},
  year={2019}
}
In this paper we study submodular maximization under a matroid constraint in the adaptive complexity model. This model was recently introduced in the context of submodular optimization to quantify the information theoretic complexity of black-box optimization in a parallel computation model. Informally, the adaptivity of an algorithm is the number of sequential rounds it makes when each round can execute polynomially-many function evaluations in parallel. Since submodular optimization is… Expand
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