A Gradient-free Penalty ADMM for Solving Distributed Convex Optimization Problems with Feasible Set Constraints-Reference-Cited by-同舟云学术

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A Gradient-free Penalty ADMM for Solving Distributed Convex Optimization Problems with Feasible Set Constraints

Published:2022-12-11 Issue: Volume: Page:
ISSN:
Container-title:2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV)
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Author:

Liu Chenyang¹,Dou Xiaohua²,Cheng Songsong¹,Fan Yuan¹

Affiliation:

1. School of Electrical Engineering and Automation, Anhui University,Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment,Hefei,Anhui,China,230601

2. Jiuquan Satellite Launching Center,Department of Techniques,Lanzhou,Gansu,China,732750

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10004205/10004219/10004351.pdf?arnumber=10004351

Reference36 articles.

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

2. Optimal Rates for Zero-Order Convex Optimization: The Power of Two Function Evaluations

3. Exponential convergence of distributed primal–dual convex optimization algorithm without strong convexity

4. The Perron-Frobenius theorem: some of its applications

5. A Smooth Double Proximal Primal-Dual Algorithm for a Class of Distributed Nonsmooth Optimization Problems

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