Xiao, X.; Deng, H.; Xu, Y. Distributed Jacobi-Proximal ADMM for Consensus Convex Optimization. Preprints2024, 2024012201. https://doi.org/10.20944/preprints202401.2201.v1
APA Style
Xiao, X., Deng, H., & Xu, Y. (2024). Distributed Jacobi-Proximal ADMM for Consensus Convex Optimization. Preprints. https://doi.org/10.20944/preprints202401.2201.v1
Chicago/Turabian Style
Xiao, X., Hui Deng and Yang-Dong Xu. 2024 "Distributed Jacobi-Proximal ADMM for Consensus Convex Optimization" Preprints. https://doi.org/10.20944/preprints202401.2201.v1
Abstract
In this paper, a distributed algorithm is proposed to solve a consensus convex optimization problem. It is a Jacobi-proximal alternating direction method of multipliers with a damping parameter $\gamma$ in the iteration of multiplier. Compared with existing algorithms, it has the following nice properties: (1) The restriction on proximal matrix is relaxed substantively, thus alleviating the weight of the proximal term. Therefore, the algorithm has a faster convergence speed. (2) The convergence analysis of the algorithm is established for any damping parameter $\gamma\in(0,2]$, which is larger ones in the literature. In addition, some numerical experiments and an application to a logistic regression problem are provided to validate the effectiveness and the characteristics of the proposed algorithm.
Computer Science and Mathematics, Computational Mathematics
Copyright:
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