Web: http://arxiv.org/abs/1907.00497

Jan. 31, 2022, 2:11 a.m. | Hakan Gokcesu, Suleyman S. Kozat

cs.LG updates on arXiv.org arxiv.org

We introduce an online convex optimization algorithm using projected
subgradient descent with optimal adaptive learning rates, with sequential and
efficient first-order updates. Our method provides a subgradient adaptive
minimax optimal dynamic regret guarantee for a sequence of general convex
functions with no known additional properties such as strong-convexity,
smoothness, exp-concavity or even Lipschitz-continuity. The guarantee is
against any comparator decision sequence with bounded "complexity", defined by
the cumulative distance traveled via changes between successive decisions. We
show optimality by generating …

arxiv math minimax online optimization

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