Practical $0.385$-Approximation for Submodular Maximization Subject to a Cardinality Constraint

Murad Tukan, Loay Mualem, Moran Feldman

Keywords: Submodular maximization, Discrete optimization, Machine learning

TL;DR: In this work, we present a novel algorithm for submodular maximization subject to a cardinality constraint that combines a guarantee of $0.385$-approximation with a low and practical query complexity of $O(n+k^2)$.

Abstract: Non-monotone constrained submodular maximization plays a crucial role in various machine learning applications. However, existing algorithms often struggle with a trade-off between approximation guarantees and practical efficiency. The current state-of-the-art is a recent $0.401$-approximation algorithm, but its computational complexity makes it highly impractical. The best practical algorithms for the problem only guarantee $1/e$-approximation. In this work, we present a novel algorithm for submodular maximization subject to a cardinality constraint that combines a guarantee of $0.385$-approximation with a low and practical query complexity of $O(n+k^2)$. Furthermore, we evaluate our algorithm's performance through extensive machine learning applications, including Movie Recommendation, Image Summarization, and more. These evaluations demonstrate the efficacy of our approach.

Primary Area: Optimization (convex and non-convex, discrete, stochastic, robust)

Submission Number: 19692