Upper norm bounds for the inverse of locally doubly strictly diagonally dominant matrices with its applications in linear complementarity problems

被引：0

作者：

Jianzhou Liu

Qi Zhou

Yebo Xiong

机构：

[1] Xiangtan University,School of Mathematics and Computational Science

[2] Xiangtan University,Hunan Key Laboratory for Computation and Simulation in Science and Engineering

[3] Hunan First Normal University,School of Mathematics and Computational Science

来源：

Numerical Algorithms | 2022年 / 90卷

关键词：

Strictly diagonally dominant (SDD) matrices; Doubly strictly diagonally dominant (DSDD) matrices; Locally doubly strictly diagonally dominant (LDSDD) matrices; Nekrasov matrices; Inverse; Infinity norm bound; Linear complementarity problems (LCPs);

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摘要：

In this paper, we present two error bounds for the linear complementarity problems (LCPs) of locally doubly strictly diagonally dominant (LDSDD) matrices. The error bounds are based on two upper norm bounds for the inverse of LDSDD matrices by using a new reduction method. The core of the reduction method lies in the particularity of the LDSDD matrices, which allows us to turn the problem into computing the counterpart of k-order doubly strictly diagonally dominant (DSDD) matrices through partition and summation. Many numerical experiments with lots of random matrices are presented to show the efficiency and superiority of our results.

引用

页码：1465 / 1491

页数：26