An effective sparse approximate inverse preconditioner for multilevel fast multipole algorithm

被引：0
|
作者：
Yang P. [1 ]
Liu J. [2 ]
Li Z. [2 ]
机构：
[1] Beijing Polytechnic, Beijing
[2] State Key Laboratory of Media Convergence and Communication, School of Information and Communication Engineering, Communication University of China, Beijing
来源：
Progress In Electromagnetics Research M | 2020年 / 98卷
基金：
中国国家自然科学基金;
关键词：
13;
D O I：
10.2528/PIERM20091105
中图分类号：
学科分类号：
摘要：
In the iterative solution of the matrix equation arising from the multilevel fast multipole algorithm (MLFMA), sparse approximate inverse (SAI) preconditioner is widely employed to improve convergence property. In this paper, based on the geometric information of nearby basis functions pairs and finer octree grouping scheme, a new sparse pattern selecting strategy for SAI is proposed to enhance robustness and efficiency. Compared to the conventional selecting strategies, the proposed strategy has only one variable parameter instructing the constructing time and memory usage, which is more user friendly. Numerical results show that the proposed strategy can make use of the non-zero entries of near-field matrix in MLFMA more effectively and elaborately without compromising the numerical accuracy and the natural parallelization of SAI. © 2020, Electromagnetics Academy. All rights reserved.
引用
收藏
页码：67 / 75
页数：8
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