Bicriteria Algorithms for Submodular Cover with Partition and Fairness Constraints

TL;DR

This paper introduces scalable bicriteria approximation algorithms for submodular cover problems with partition-based constraints, addressing fairness and resource balance across dataset partitions, and demonstrates their effectiveness through empirical evaluation.

Contribution

It develops and analyzes the first scalable bicriteria algorithms for submodular cover with partition constraints, achieving optimal guarantees for monotone cases and reducing query complexity.

Findings

Algorithms achieve optimal approximation guarantees for monotone objectives.

Proposed methods significantly reduce query complexity compared to existing approaches.

Empirical results validate efficiency and effectiveness on real-world datasets.

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.