Online Optimization of Discrete Convex Functions with Bounded Domains

This paper introduces the problem of online optimization of L♮-convex functions, which generalize online submodular minimization to a broader class of discrete convex functions. The authors propose computationally efficient algorithms with tight regret bounds for both the full information and bandit settings.