Data-dependent Evaluations for Budgeted Submodular Maximization
Submodular maximization is an important building block for developing algorithms in many areas such as machine learning and data mining. Due to the NP-hardness of the problem, analysis of submodular maximization algorithms typically provides pessimistic worst-case approximation factors only. It is not easy to evaluate how close a produced solution is to an optimal one for a given problem instance. In this paper, we develop new data-dependent upper bounds for submodular maximization with a knapsack constraint. We theoretically prove that they dominate the optimal solution and empirically demonstrate their advantages in certifying how close to optimal a solution is through experiments with ...
| Authors | Lejian Zhang, Xueyan Tang, Jing Tang |
|---|---|
| Primary category | cs.DS |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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