# Pareto Optimization for Subset Selection with Dynamic Partition Matroid Constraints

arXiv: https://arxiv.org/abs/2012.08738 · HTML: https://mlsanity.com/paper/2012.08738/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees](https://mlsanity.com/paper/2110.09332/) (2021)
- [Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences](https://mlsanity.com/paper/2104.09884/) (2021)
- [Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler](https://mlsanity.com/paper/2302.01464/) (2023)

## Abstract (arXiv)

In this study, we consider the subset selection problems with submodular or monotone discrete objective functions under partition matroid constraints where the thresholds are dynamic. We focus on POMC, a simple Pareto optimization approach that has been shown to be effective on such problems. Our analysis departs from singular constraint problems and extends to problems of multiple constraints. We show that previous results of POMC's performance also hold for multiple constraints. Our experimental investigations on random undirected maxcut problems demonstrate POMC's competitiveness against the classical GREEDY algorithm with restart strategy.