.png){kind=icon}
返回
A Primal-Dual Forward-Backward Splitting Algorithm for Distributed Convex Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TETCI.2021.3098831.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Motivated by modern large-scale information processing problems in engineering, this paper concentrates on studying distributed constrained convex optimization problems over a connected undirected network. The problem involves a sum of a differentiable convex function with Lipschitz continuous gradient and two non-smooth convex functions with a linear operator. To solve such a problem, we propose a novel distributed primal-dual forward-backward splitting algorithm, called D-PDFBS. Each agent locally computes the Lipschitz continuous gradient and two proximal operators, and exchanges information with its neighbors. D-PDFBS adopts non-identical stepsizes, and we reveal the relationship between selection of stepsizes and parameters of objective functions. The simulation results verify the feasibility of D-PDFBS and the correctness of theoretical findings.
Keyword:
Optimization
Convex functions
Convergence
Wireless sensor networks
Wireless communication
Symmetric matrices
Smart grids
Forward-backward splitting method
distributed optimization
primal-dual algorithm
non-smooth function
期刊
I
IF:
6.5
论文数:
1.4K
被引数:
4.5K
机构
引用论文
.png){kind=icon}
分享收藏A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization坐标下降原始对偶算法及其在分布式异步优化中的应用
OAAI
OAAI分享收藏Electrochromic 2,4,6-triphenyl-1,3,5-triazine based esters with electron donor-acceptor structure
PREAI
分享收藏
查看更多内容