Preconditioned inexact Jacobi-Davidson method for large symmetric eigenvalue problems

被引：0

作者：

Miao, Hong-Yi ^{[1
,2
]}

Wang, Li ^{[1
]}

机构：

[1] Nanjing Normal Univ, Sch Math Sci, NSLSCS, Jiangsu Key Lab, Nanjing 210046, Peoples R China

[2] Nanjing Forestry Univ, Coll Sci, Nanjing 210037, Peoples R China

来源：

COMPUTATIONAL & APPLIED MATHEMATICS | 2020年 / 39卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Eigenvalues; Symmetric matrix; Inexact Jacobi-Davidson method; Saddle point matrix; Preconditioner; SHIFT-SPLITTING PRECONDITIONERS; NUMERICAL-SOLUTION; ITERATION; EQUATIONS;

D O I：

10.1007/s40314-020-01172-0

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

A preconditioned inexact Jacobi-Davidson method for the computation of the eigenpairs of large and sparse symmetric matrices is proposed. In each inner iteration step of Jacobi-Davidson method, two preconditioners based on a regularization method and shift-splitting of the saddle point matrix are given. Then the properties of the preconditioned matrix are investigated. Numerical results illustrate that the new proposed algorithms are more efficient than Jacobi-Davidson method.

引用

页数：13

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