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Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint
Georgios Amanatidis · Federico Fusco · Philip Lazos · Stefano Leonardi · Rebecca Reiffenhäuser

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #514
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. Moreover, 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 knapsack constraint. We present a simple randomized greedy algorithm that achieves a $5.83$ approximation and runs in $O(n \log n)$ time, i.e., at least a factor $n$ faster than other state-of-the-art algorithms. The robustness of our approach allows us to further transfer it to a stochastic version of the problem. There, we obtain a 9-approximation to the best adaptive policy, which is the first constant approximation for non-monotone objectives. Experimental evaluation of our algorithms showcases their improved performance on real and synthetic data.

Author Information

Georgios Amanatidis (University of Essex)
Federico Fusco (Sapienza University of Rome)
Philip Lazos (Sapienza University of Rome)
Stefano Leonardi (Sapienza University of Rome)
Rebecca Reiffenhäuser (Sapienza University of Rome)

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