# Submodular Maximization Through Barrier Functions

arXiv: https://arxiv.org/abs/2002.03523 · HTML: https://mlsanity.com/paper/2002.03523/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity](https://mlsanity.com/paper/2102.08327/) (2021)
- [Lyapunov function approach for approximation algorithm design and analysis: with applications in submodular maximization](https://mlsanity.com/paper/2205.12442/) (2022)
- [The Power of Subsampling in Submodular Maximization](https://mlsanity.com/paper/2104.02772/) (2021)
- [Submodular Maximization under the Intersection of Matroid and Knapsack Constraints](https://mlsanity.com/paper/2307.09487/) (2023)
- [Practical Parallel Algorithms for Non-Monotone Submodular Maximization](https://mlsanity.com/paper/2308.10656/) (2023)

## Abstract (arXiv)

In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves the running time for constrained submodular maximization but also provides the state of the art guarantee. More precisely, for maximizing a monotone submodular function subject to the combination of a k-matchoid and ℓ-knapsack constraint (for ℓ≤ k), we propose a potential function that can be approximately minimized. Once we minimize the potential function up to an ε error it is guaranteed that we have found a feasible set with a 2(k+1+ε)-approximation factor which can indeed be further improved to (k+1+ε) by an enumeration technique. We extensively evaluate the performance of our proposed algorithm over several real-world applications, including a movie recommendation system, summarization tasks for YouTube videos, Twitter feeds and Yelp business locations, and a set cover problem.