Title
A new greedy strategy for maximizing monotone submodular function under a cardinality constraint
Abstract
In this paper, we study the problem of maximizing a monotone non-decreasing submodular function $$f :2^\Omega \rightarrow {\mathbb {R}}_{+}$$ subject to a cardinality constraint, i.e., $$\max \{ f(A) : |A| \le k, A \subseteq \Omega \} $$ . We propose a deterministic algorithm based on a new greedy strategy for solving this problem. We prove that when the objective function f satisfies certain assumptions, the algorithm we propose can return a $$1 - \kappa _f (1 - \frac{1}{k})^k$$ approximate solution with O(kn) value oracle queries, where $$\kappa _f$$ is the curvature of the monotone submodular function f. We also show that our algorithm outperforms the traditional greedy algorithm in some cases. Furthermore, we demonstrate how to implement our algorithm in practice.
Year
DOI
Venue
2022
10.1007/s10898-021-01103-1
Journal of Global Optimization
Keywords
DocType
Volume
Submodular Maximization, Greedy Algorithm, Cardinality Constraint, Curvature
Journal
83
Issue
ISSN
Citations 
2
0925-5001
0
PageRank 
References 
Authors
0.34
9
3
Name
Order
Citations
PageRank
Cheng Lu100.68
Wenguo Yang2374.43
Suixiang Gao300.68