Boosting One-Point Derivative-Free Online Optimization via Residual Feedback-Reference-Cited by-同舟云学术

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Boosting One-Point Derivative-Free Online Optimization via Residual Feedback

Published:2024-09 Issue:9 Volume:69 Page:6309-6316
ISSN:0018-9286
Container-title:IEEE Transactions on Automatic Control
language:
Short-container-title:IEEE Trans. Automat. Contr.

Author:

Zhang Yan¹^ORCID,Zhou Yi²^ORCID,Ji Kaiyi³^ORCID,Shen Yi¹^ORCID,Zavlanos Michael M.¹^ORCID

Affiliation:

1. Department of Mechanical Engineering and Material Science, Duke University, Durham, NC, USA

2. Department of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT, USA

3. Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA

Funder

Air Force Office of Scientific Research

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

https://ieeexplore.ieee.org/ielam/9/10654305/10480627-aam.pdf

Reference32 articles.

1. ZOO

2. Global convergence of policy gradient methods for the linear quadratic regulator;Fazel,2018

3. Derivative-free methods for policy optimization: Guarantees for linear quadratic systems;Malik,2019

4. Optimal algorithms for online convex optimization with multi-point bandit feedback;Agarwal,2010

5. Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming

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1. Decentralized Online Bandit Federated Learning Over Unbalanced Directed Networks;IEEE Transactions on Network Science and Engineering;2024-09

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