Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match

NeurIPS 2019 Amin KarbasiHamed HassaniAryan MokhtariZebang Shen

In this paper, we develop \scg~(\text{SCG}{$++$}), the first efficient variant of a conditional gradient method for maximizing a continuous submodular function subject to a convex constraint. Concretely, for a monotone and continuous DR-submodular function, \SCGPP achieves a tight $[(1-1/e)\OPT -\epsilon]$ solution while using $O(1/\epsilon^2)$ stochastic gradients and $O(1/\epsilon)$ calls to the linear optimization oracle... (read more)

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