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This research paper evaluates frontier LLMs as batch optimizers in both continuous and discrete settings, finding them competitive in numerical tasks but more effective in semantically rich environments compared to classical methods.
This paper presents an online algorithm for maximizing non-monotone DR-submodular functions under down-closed convex constraints, achieving the best-known offline approximation factor of 0.401 with sublinear regret in the full-information value-oracle model.