A Note On Deterministic Submodular Maximization With Bounded Curvature
arXiv:2409.02943v1h-index: 1
Originality Synthesis-oriented
AI Analysis
This work addresses an incremental improvement in optimization algorithms for researchers in theoretical computer science and machine learning.
The paper tackles the problem of deterministic submodular maximization under matroid constraints by leveraging a recent breakthrough to achieve a (1-κ_f/e-ε)-approximation algorithm, where κ_f is the curvature, providing a concrete approximation guarantee.
We show that the recent breakthrough result of [Buchbinder and Feldman, FOCS'24] could further lead to a deterministic $(1-κ_{f}/e-\varepsilon)$-approximate algorithm for maximizing a submodular function with curvature $κ_{f}$ under matroid constraint.