Sublinear and Linear Convergence of Modified ADMM for Distributed Nonconvex Optimization-Reference-Cited by-同舟云学术

Sublinear and Linear Convergence of Modified ADMM for Distributed Nonconvex Optimization

Published:2023-03 Issue:1 Volume:10 Page:75-86
ISSN:2325-5870
Container-title:IEEE Transactions on Control of Network Systems
language:
Short-container-title:IEEE Trans. Control Netw. Syst.

Author:

Yi Xinlei¹^ORCID,Zhang Shengjun²^ORCID,Yang Tao³^ORCID,Chai Tianyou³^ORCID,Johansson Karl Henrik¹^ORCID

Affiliation:

1. Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden

2. Department of Electrical Engineering, University of North Texas, Denton, TX, USA

3. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China

Funder

Knut och Alice Wallenbergs Stiftelse

Swedish Foundation for Strategic Research

Vetenskapsrådet

National Natural Science Foundation of China

2020 Science and Technology Major Project of Liaoning Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Control and Optimization,Computer Networks and Communications,Signal Processing,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/6509490/10075640/09808119.pdf?arnumber=9808119

Reference54 articles.

1. Distributed Gradient Descent: Nonconvergence to Saddle Points and the Stable-Manifold Theorem

2. Perturbed proximal primal–dual algorithm for nonconvex nonsmooth optimization

3. Distributed Non-Convex First-Order Optimization and Information Processing: Lower Complexity Bounds and Rate Optimal Algorithms

4. Distributed Stochastic Gradient Method for Non-Convex Problems with Applications in Supervised Learning

5. Distributed Zero-Order Algorithms for Nonconvex Multiagent Optimization

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