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We study online convex optimization in a setting where the learner seeks to minimize the sum of a per-round hitting cost and a movement cost which is incurred when changing decisions between rounds.
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A 2-competitive algorithm for online convex optimization with switching costs
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Using predictions in online optimization: Looking forward with an eye on the past
N. Chen, J. Comden, Z. Liu, A. Gandhi, and A. Wierman · 2016
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Nested convex bodies are chaseable
N. Bansal, M. Böhm, M. Eliáš, G. Koumoutsos, and S. W. Umboh · 2018
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Better bounds for online line chasing
M. Bienkowski, J. Byrka, M. Chrobak, C. Coester, L. Jez, and E. Koutsoupias · 2018
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k-server via multiscale entropic regularization
S. Bubeck, M. B. Cohen, Y. T. Lee, J. R. Lee, and A. Mądry · 2018
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Smoothed online convex optimization in high dimensions via online balanced descent
N. Chen, G. Goel, and A. Wierman · 2018
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Using predictions in online optimization with switching costs: A fast algorithm and a fundamental limit
Y. Li, G. Qu, and N. Li · 2018
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A nearly-linear bound for chasing nested convex bodies
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A tight lower bound for online convex optimization with switching costs
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