.png){kind=icon}
返回
Distributed Stochastic Proximal Algorithm With Random Reshuffling for Nonsmooth Finite-Sum Optimization
.png){kind=icon}
OA
AI
DOI：10.1109/TNNLS.2022.3201711.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The nonsmooth finite-sum minimization is a fundamental problem in machine learning. This article develops a distributed stochastic proximal-gradient algorithm with random reshuffling to solve the finite-sum minimization over time-varying multiagent networks. The objective function is a sum of differentiable convex functions and nonsmooth regularization. Each agent in the network updates local variables by local information exchange and cooperates to seek an optimal solution. We prove that local variable estimates generated by the proposed algorithm achieve consensus and are attracted to a neighborhood of the optimal solution with an O((1/T)+(1/root T)) convergence rate, where T is the total number of iterations. Finally, some comparative simulations are provided to verify the convergence performance of the proposed algorithm.
Keyword:
Distributed optimization
proximal operator
random reshuffling (RR)
stochastic algorithm
time-varying graphs
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Stability analysis of distributed convex optimization under persistent attacks: A hybrid systems approach.png)
AUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAI分享收藏Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏Convergence analysis of distributed stochastic gradient descent with shuffling带shuffling的分布随机梯度下降算法的收敛性分析NEUROCOMPUTINGOAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏
查看更多内容