matroid-constraints

This paper presents a unified algorithmic framework for distributed online submodular maximization under partition matroid constraints, achieving sublinear (1-1/e)-regret guarantees for both full-information and bandit feedback. It also introduces a bounded stochastic pipage rounding scheme to ensure cumulative sampling violations remain sublinear.