Optimization of chance-constrained submodular functions
Date
2020
Authors
Doerr, B.
Doerr, C.
Neumann, A.
Neumann, F.
Sutton, A.M.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2020, vol.34, iss.2, pp.1460-1467
Statement of Responsibility
Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton
Conference Name
Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI) (7 Feb 2020 - 12 Feb 2020 : New York, NY, USA)
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.
School/Discipline
Dissertation Note
Provenance
Description
AAAI-20 Technical Tracks 2 / AAAI Technical Track: Constraint Satisfaction and Optimization
Access Status
Rights
Copyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.