Bicriteria Algorithms for Submodular Cover with Partition and Fairness Constraints

ICLR 2026 Conference Submission22551 Authors

Keywords: submodular cover, fairness, matroid constraint, submodular optimization

Abstract:

In many submodular optimization applications, datasets are naturally partitioned into disjoint subsets. These scenarios give rise to submodular optimization problems with partition-based constraints, where the desired solution set should be in some sense balanced, fair, or resource-constrained across these partitions. While existing work on submodular cover largely overlooks this structure, we initiate a comprehensive study of the problem of Submodular Cover with Partition Constraints (SCP) and its key variants. Our main contributions are the development and analysis of scalable bicriteria approximation algorithms for these NP-hard optimization problems for both monotone and nonmonotone objectives. Notably, the algorithms proposed for the monotone case achieve optimal approximation guarantees while significantly reducing query complexity compared to existing methods.
Finally, empirical evaluations on
real-world and synthetic datasets further validate the efficiency and effectiveness
of the proposed algorithms.

Primary Area: optimization

Submission Number: 22551