Infinity Norm Bounds for the Inverse of \(GSDD_k\) Matrices with Applications

In this paper, we investigate a new subclass of nonsingular \(H\)-matrices called \(GSDD_k\) matrices. The relationships between \(GSDD_k\) matrices and other subclasses of nonsingular \(H\)-matrices are analyzed. Moreover, the infinity norm bounds for the inverse of \(GSDD_k\) matrices are obtained. Based on the relationship between strictly diagonally dominant (\(SDD\)) matrices and \(GSDD_k\) matrices, infinity norm bounds for the inverse of \(SDD\) matrices are also derived, which improve some existing bounds. Furthermore, error bounds of linear complementarity problems involving \(GSDD_k\) matrices and \(SDD\) matrices are given. Numerical examples demonstrate the effectiveness of the new results.

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Publication Details

Journal
Communications in Mathematical Research
Published
2026-10-26
DOI
https://doi.org/10.4208/cmr.2026-0040
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

Infinity Norm Bounds for the Inverse of \(GSDD_k\) Matrices with Applications

Hongmin Mo, Xiaomin Chen
Communications in Mathematical Research
Matrix Theory and Algorithms
article

Infinity Norm Bounds for the Inverse of \(GSDD_k\) Matrices with Applications

Hongmin Mo, Xiaomin Chen
article en

Abstract

In this paper, we investigate a new subclass of nonsingular \(H\)-matrices called \(GSDD_k\) matrices. The relationships between \(GSDD_k\) matrices and other subclasses of nonsingular \(H\)-matrices are analyzed. Moreover, the infinity norm bounds for the inverse of \(GSDD_k\) matrices are obtained. Based on the relationship between strictly diagonally dominant (\(SDD\)) matrices and \(GSDD_k\) matrices, infinity norm bounds for the inverse of \(SDD\) matrices are also derived, which improve some existing bounds. Furthermore, error bounds of linear complementarity problems involving \(GSDD_k\) matrices and \(SDD\) matrices are given. Numerical examples demonstrate the effectiveness of the new results.

Communications in Mathematical Research
Jishou University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
Matrix Theory and Algorithms
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