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  • title: A gradient sampling method with complexity guarantees for Lipschitz functions in low and high dimensions
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            A gradient sampling method with complexity guarantees for Lipschitz functions in low and high dimensions
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            A gradient sampling method with complexity guarantees for Lipschitz functions in low and high dimensions

            Nov 28, 2022

            Speakers

            DD

            Damek Davis

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            DD

            Dmitriy Drusvyatskiy

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            YTL

            Yin Tat Lee

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            About

            Zhang et al. (ICML 2020) introduced a novel modification of Goldstein's classical subgradient method, with an efficiency guarantee of O(ε^-4) for minimizing Lipschitz functions. Their work, however, makes use of an oracle that is not efficiently implementable. In this paper, we obtain the same efficiency guarantee with a standard subgradient oracle, thus making our algorithm efficiently implementable. Our resulting method works on any Lipschitz function whose value and gradient can be evaluated…

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            NeurIPS 2022

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