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Online Submodular Maximization via Online Convex Optimization by Salem, Tareq Si; Özcan, Gözde; Nikolaou, Iasonas; Terzi, Evimaria; Ioannidis, Stratis is a scholarly article available to read on EtoBox.

What is Online Submodular Maximization via Online Convex Optimization about?

We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, weighted threshold potential functions, reduces to online convex optimization (OCO). This is precisely because functions in this class admit a concave relaxation; as a result, OCO policies, coupled with an appropriate rounding scheme, can be used to achieve sublinear regret in the combinatorial setting. We show that our reduction extends to many different versions of the online learning problem, including the dynamic regret, bandit, and optimistic-learning settings.

Author

Salem, Tareq Si; Özcan, Gözde; Nikolaou, Iasonas; Terzi, Evimaria; Ioannidis, Stratis

Published

2023

Language

EN