利用缩放矩阵的Nekrasov矩阵逆的无穷范数界
Infinity norm bounds for the inverse of Nekrasov matrices using scaling matrices
AI总结：
该研究针对Nekrasov矩阵，利用缩放矩阵将其转化为严格对角占优矩阵，得到逆矩阵的无穷范数上界，还推导了线性互补问题的新误差界，效果优于已有结果。
摘要：
在诸多应用中，获取给定矩阵逆的范数的优良上界十分实用。本文针对矩阵A为Nekrasov矩阵的情形，通过缩放矩阵将A转化为严格对角占优矩阵，得到了此类上界。文中包含数值算例以及与其他界的对比结果。这类缩放矩阵还被用于推导当涉及矩阵为Nekrasov矩阵时线性互补问题的新误差界，这些误差界可显著改进此前的其他界。
英文摘要：
For many applications, it is convenient to have good upper bounds for the norm of the inverse of a given matrix. In this paper, we obtain such bounds when A is a Nekrasov matrix, by means of a scaling matrix transforming A into a strictly diagonally dominant matrix. Numerical examples and comparisons with other bounds are included. The scaling matrices are also used to derive new error bounds for the linear complementarity problems when the involved matrix is a Nekrasov matrix. These error bounds can improve considerably other previous bounds.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
