共 39 条
A relaxed generalized Newton iteration method for generalized absolute value equations
被引:14
作者:
Cao, Yang
[1
]
Shi, Quan
[1
]
Zhu, Sen-Lai
[1
]
机构:
[1] Nantong Univ, Sch Transportat & Civil Engn, Nantong 226019, Peoples R China
来源:
AIMS MATHEMATICS
|
2021年
/
6卷
/
02期
基金:
中国国家自然科学基金;
关键词:
generalized absolute value equations;
Newton method;
relaxation;
globally convergence;
LINEAR COMPLEMENTARITY;
CONVERGENCE;
ALGORITHM;
D O I:
10.3934/math.2021078
中图分类号:
O29 [应用数学];
学科分类号:
070104 ;
摘要:
To avoid singular generalized Jacobian matrix and further accelerate the convergence of the generalized Newton (GN) iteration method for solving generalized absolute value equations Ax - B vertical bar x vertical bar = b, in this paper we propose a new relaxed generalized Newton (RGN) iteration method by introducing a relaxation iteration parameter. The new RGN iteration method involves the well-known GN iteration method and the Picard iteration method as special cases. Theoretical analyses show that the RGN iteration method is well defined and globally linearly convergent under suitable conditions. In addition, a specific sufficient condition is studied when the coefficient matrix A is symmetric positive definite. Finally, two numerical experiments arising from the linear complementarity problems are used to illustrate the effectiveness of the new RGN iteration method.
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页码:1258 / 1275
页数:18
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