Accelerated Dual Averaging Methods for Decentralized Constrained Optimization-Reference-Cited by-同舟云学术

Accelerated Dual Averaging Methods for Decentralized Constrained Optimization

Published:2023-04 Issue:4 Volume:68 Page:2125-2139
ISSN:0018-9286
Container-title:IEEE Transactions on Automatic Control
language:
Short-container-title:IEEE Trans. Automat. Contr.

Author:

Liu Changxin¹^ORCID,Shi Yang²^ORCID,Li Huiping³^ORCID,Du Wenli¹^ORCID

Affiliation:

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

2. Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada

3. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China

Funder

Natural Sciences and Engineering Research Council of Canada

National Natural Science Foundation of China

Shaanxi Provincial Funds for Distinguished Young Scientists

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9/10084346/09770368.pdf?arnumber=9770368

Reference45 articles.

1. A dual approach for optimal algorithms in distributed optimization over networks

2. On acceleration with noise-corrupted gradients;cohen;Proc Int Conf Mach Learn,0

3. On the Linear Convergence of the ADMM in Decentralized Consensus Optimization

4. Gossip dual averaging for decentralized optimization of pairwise functions;colin;Proc Int Conf Mach Learn,0

5. Optimal algorithms for smooth and strongly convex distributed optimization in networks;scaman;Proc Int Conf Mach Learn,0

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