The optimal dynamic regret for smoothed online convex optimization with squared <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si18.svg"><mml:msub><mml:mi>l</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math> norm switching costs-Reference-Cited by-同舟云学术

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The optimal dynamic regret for smoothed online convex optimization with squared l2 norm switching costs

Published:2023-04 Issue:6 Volume:360 Page:4297-4330
ISSN:0016-0032
Container-title:Journal of the Franklin Institute
language:en
Short-container-title:Journal of the Franklin Institute

Author:

Zhang Yaoyu^ORCID,Liu Qingsong,Sun Jian^ORCID,Wu Chenye^ORCID

Funder

National Natural Science Foundation of China

Science, Technology and Innovation Commission of Shenzhen Municipality

Publisher

Elsevier BV

Subject

Applied Mathematics,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Reference33 articles.

1. Improved dynamic regret for non-degenerate functions;Zhang,2017

2. Dynamic regret of convex and smooth functions;Zhao;Adv. Neural Inf. Process. Syst.,2020

3. Simultaneously achieving sublinear regret and constraint violations for online convex optimization with time-varying constraints;Liu;Perform. Eval.,2021

4. Online optimization in cloud resource provisioning: predictions, regrets, and algorithms;Comden;Proc. ACM Meas. Anal. Comput. Syst.,2019

5. Combinatorial bandits with linear constraints: beyond knapsacks and fairness;Liu,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Online composite optimization with time-varying regularizers;Journal of the Franklin Institute;2024-07

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