Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, Zhejaing Province, PR China.
Department of Applied Mathematics, College of Sciences, China Jiliang University, Hangzhou 310018, Zhejaing Province, PR China.
Neural Netw. 2018 May;101:94-100. doi: 10.1016/j.neunet.2018.02.006. Epub 2018 Feb 15.
It is well known that the support vector machine (SVM) is an effective learning algorithm. The alternating direction method of multipliers (ADMM) algorithm has emerged as a powerful technique for solving distributed optimisation models. This paper proposes a distributed SVM algorithm in a master-slave mode (MS-DSVM), which integrates a distributed SVM and ADMM acting in a master-slave configuration where the master node and slave nodes are connected, meaning the results can be broadcasted. The distributed SVM is regarded as a regularised optimisation problem and modelled as a series of convex optimisation sub-problems that are solved by ADMM. Additionally, the over-relaxation technique is utilised to accelerate the convergence rate of the proposed MS-DSVM. Our theoretical analysis demonstrates that the proposed MS-DSVM has linear convergence, meaning it possesses the fastest convergence rate among existing standard distributed ADMM algorithms. Numerical examples demonstrate that the convergence and accuracy of the proposed MS-DSVM are superior to those of existing methods under the ADMM framework.
众所周知，支持向量机（SVM）是一种有效的学习算法。交替方向乘子法（ADMM）算法已经成为解决分布式优化模型的强大技术。本文提出了一种主从式的分布式 SVM 算法（MS-DSVM），它将分布式 SVM 和 ADMM 集成在主从配置中，其中主节点和从节点相连，这意味着结果可以广播。分布式 SVM 被视为正则化优化问题，并建模为一系列通过 ADMM 求解的凸优化子问题。此外，还利用过松弛技术来加速所提出的 MS-DSVM 的收敛速度。我们的理论分析表明，所提出的 MS-DSVM 具有线性收敛性，这意味着它在现有的标准分布式 ADMM 算法中具有最快的收敛速度。数值例子表明，在 ADMM 框架下，所提出的 MS-DSVM 的收敛性和准确性优于现有的方法。
