Linear convergence of a primal-dual algorithm for distributed interval optimization-Reference-Cited by-同舟云学术

Linear convergence of a primal-dual algorithm for distributed interval optimization

Published:2024 Issue:2 Volume:32 Page:857-873
ISSN:2688-1594
Container-title:Electronic Research Archive
language:
Short-container-title:era

Author:

Wang Yinghui¹²,Wang Jiuwei³⁴,Song Xiaobo¹²,Hu Yanpeng¹²

Affiliation:

1. Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

2. Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

3. University of Chinese Academy of Sciences, Beijing 100049, China

4. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Abstract

<abstract><p>In this paper, we investigate a distributed interval optimization problem whose local functions are interval functions rather than scalar functions. Focusing on distributed interval optimization, this paper presents a distributed primal-dual algorithm. A criterion is introduced under which linear convergence to the Pareto solution of distributed interval optimization problems can be achieved without strong convexity. Lastly, a numerical simulation is presented to illustrate the linear convergence of the algorithm that has been proposed.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

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