文献类型：期刊文献

中文题名：求解鞍点问题的广义正定和反Hermitian分裂方法

英文题名：A generalized positive-definite and skew-Hermitian splitting iteration method for saddle point problems

作者：董贝贝[1];鲍亮[1]

机构：[1]华东理工大学理学院

年份：2019

卷号：41

期号：9

起止页码：1567

中文期刊名：计算机工程与科学

外文期刊名：Computer Engineering & Science

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

语种：中文

中文关键词：鞍点问题;GPSS;收敛性;正定分裂

外文关键词：saddle-point problem;generalization of the positive-definite and skew-Hermitian splitting (GPSS);convergence;positive splitting

摘要：探讨了如何求解大型稀疏鞍点问题,给出了一种基于正定分裂的广义正定和反Hermitian分裂(GPSS)方法。该方法首先利用矩阵的正定分裂,构造出鞍点矩阵的2种分裂格式;然后利用这2种分裂格式构造出GPSS迭代;接着给出了迭代收敛的充要条件。最后进行了数值对比实验,实验结果表明,GPSS比正定和反Hermitian分裂(PSS)和Hermitian和反Hermitian分裂(HSS)方法更有效。
We propose a generalized positive-definite and skew-Hermitian splitting method to solve the large sparse saddle point problems based on the positive-definite splitting. The method first uses the positive-definite splitting of matrix to construct two splitting forms of the saddle-point matrix. Then, the two splitting formats are used to construct the GPSS iteration. Thirdly, the necessary and sufficient conditions for the iteration convergence are given. Finally, some numerical comparison experiments are carried out and the results show that the GPSS is more effective than "the positive-definite and skew-Hermitian splitting"(PSS) method and "the Hermitian and skew-Hermitian splitting"(HSS) method.