Optimization of Chance-Constrained Submodular Functions - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

Optimization of Chance-Constrained Submodular Functions

Carola Doerr
Aneta Neumann
  • Function : Author
Frank Neumann
  • Function : Author
Andrew M Sutton
  • Function : Author


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.
Fichier principal
Vignette du fichier
1911.11451.pdf (280.28 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02871943 , version 1 (17-06-2020)


  • HAL Id : hal-02871943 , version 1


Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M Sutton. Optimization of Chance-Constrained Submodular Functions. AAAI-20 Thirty-Fourth AAAI Conference on Artificial Intelligence, Feb 2020, New York, United States. ⟨hal-02871943⟩
118 View
93 Download


Gmail Facebook Twitter LinkedIn More