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|Title:||Optimization of chance-constrained submodular functions|
|Citation:||Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020 / vol.34, iss.2, pp.1460-1467|
|Publisher:||Association for the Advancement of Artificial Intelligence|
|Publisher Place:||Palo Alto, CA|
|Conference Name:||Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) (07 Feb 2020 - 12 Feb 2020 : New York, NY, USA)|
|Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton|
|Abstract:||Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints. We provide a first analysis on the approximation behavior of popular greedy algorithms for submodular problems with chance constraints. Our results show that these algorithms are highly effective when using surrogate functions that estimate constraint violations based on Chernoff bounds. Furthermore, we investigate the behavior of the algorithms on popular social network problems and show that high quality solutions can still be obtained even if there are strong restrictions imposed by the chance constraint.|
|Description:||AAAI-20 Technical Tracks 2 / AAAI Technical Track: Constraint Satisfaction and Optimization|
|Rights:||Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.|
|Appears in Collections:||Computer Science publications|
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