# Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint

@article{Amanatidis2020FastAN, title={Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint}, author={Georgios Amanatidis and Federico Fusco and Philip Lazos and S. Leonardi and Rebecca Reiffenhauser}, journal={ArXiv}, year={2020}, volume={abs/2007.05014} }

Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing. The massive instances occurring in modern day applications can render existing algorithms prohibitively slow, while frequently, those instances are also inherently stochastic. Focusing on these challenges, we revisit the classic problem of maximizing a (possibly non-monotone) submodular function subject to a… Expand

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