Graph Topology Invariant Gradient and Sampling Complexity for Decentralized and Stochastic Optimization-Reference-Cited by-同舟云学术

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Graph Topology Invariant Gradient and Sampling Complexity for Decentralized and Stochastic Optimization

Published:2023-07-28 Issue:3 Volume:33 Page:1647-1675
ISSN:1052-6234
Container-title:SIAM Journal on Optimization
language:en
Short-container-title:SIAM J. Optim.

Author:

Lan Guanghui¹,Ouyang Yuyuan²^ORCID,Zhou Yi³^ORCID

Affiliation:

1. H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

2. School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29631 USA.

3. IBM Almaden Research Center, San Jose, CA 95120 USA.

Funder

Army Research Office

Office of Naval Research

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Theoretical Computer Science,Software,Applied Mathematics

Reference44 articles.

1. Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

2. Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization

3. Non-euclidean restricted memory level method for large-scale convex optimization

4. Fastest Mixing Markov Chain on a Graph

5. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

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1. Resilience to Malicious Activity in Distributed Optimization for Cyberphysical Systems;2022 IEEE 61st Conference on Decision and Control (CDC);2022-12-06

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