Adaptive Threshold-Driven Continuous Greedy Method for Scalable Submodular Optimization
Submodular maximization under matroid constraints is a fundamental problem in combinatorial optimization with applications in sensing, data summarization, active learning, and resource allocation. While the Sequential Greedy (SG) algorithm achieves only a $\frac{1}{2}$-approximation due to irrevocable selections, Continuous Greedy (CG) attains the optimal $\bigl(1-\frac{1}{e}\bigr)$-approximation via the multilinear relaxation, at the cost of a progressively dense decision vector that forces agents to exchange feature embeddings for nearly every ground-set element. We propose \textit{ATCG} (\underline{A}daptive \underline{T}hresholded \underline{C}ontinuous \underline{G}reedy), which gate...
| Authors | Mohammadreza Rostami, Solmaz S. Kia |
|---|---|
| Primary category | cs.LG |
| Published | 3 April 2026 |
| Last updated at source | 18 May 2026 |
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