Speaker：Jianzhou Liu (Xiangtan University)

Time：2021-12-11 09:00

Location：Tencent Meeting ID：653 937 001（No Passward）

Abstract:

In this paper, by partition and summation, we obtain two new upper bounds for the inverse of strictly diagonally dominant (SDD) M-matrices by reduction method. Various numerical experiments with random matrices show that the upper bound (which is expressed by means of some determinants of third order matrices) is much sharper than existing ones. In addition, the new upper bounds for the matrix inverse can be utilized for the error analysis in LCPs for error bounds, and we propose two sharper error bounds for B-matrices by computing the maximum value of multivariate functions. In addition, some other numerical experiments for error bounds of LCPs are presented to show the efficiency and superiority of our results.