Consistent Submodular Maximization

Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper, we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion, and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i. e. , the number of changes between two consecutive solutions is bounded). In this setting, we provide algorithms with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances.

Authors

• Paul Dütting
• Federico Fusco 0001
• Silvio Lattanzi
• Ashkan Norouzi-Fard
• Morteza Zadimoghaddam

Keywords

No keywords are indexed for this paper.