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Lower Bounds on the Oracle Complexity of Nonsmooth Convex Optimization via Information Theory

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Publication:5358597
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DOI10.1109/TIT.2017.2701343zbMath1370.94383arXiv1407.5144OpenAlexW2962961444MaRDI QIDQ5358597

Cristóbal Guzmán, Gábor Braun, Sebastian Pokutta

Publication date: 21 September 2017

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1407.5144



Mathematics Subject Classification ID

Convex programming (90C25) Measures of information, entropy (94A17) Information theory (general) (94A15)


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Lower Bounds for Parallel and Randomized Convex Optimization ⋮ Statistical Query Algorithms for Mean Vector Estimation and Stochastic Convex Optimization ⋮ Lower bounds for non-convex stochastic optimization ⋮ Complexity of optimizing over the integers ⋮ Combinatorial optimization. Abstracts from the workshop held November 7--13, 2021 (hybrid meeting) ⋮ Lower bounds for finding stationary points I ⋮ A discrete dynamics approach to sparse calculation and applied in ontology science




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