A transformation approach that makes SPAI, PSAI and RSAI procedures efficient for large double irregular nonsymmetric sparse linear systems

A transformation approach that makes SPAI, PSAI and RSAI procedures efficient for large double irregular nonsymmetric sparse linear systems
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一种使 SPAI、PSAI 和 RSAI 过程对于大型双不规则非对称稀疏线性系统有效的变换方法

DOI:
10.1016/j.cam.2018.08.033
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发表时间:
2015-12
影响因子:
2.4
通讯作者:
Kang Wenjie
Kang Wenjie
中科院分区:
数学2区
文献类型:
--
作者:
Jia Zhongxiao;Kang Wenjie

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如果一个稀疏矩阵至少有一个相对密集的列和行,则称为双不规则稀疏,如果它的所有列和行都是稀疏的,则称为双正则稀疏。对于系数矩阵为双不规则稀疏的大型稀疏非对称线性系统,稀疏近似逆预条件SPAI、PSAI (t ~ 1)和RSAI (t ~ 1)构造预条件成本高,甚至不切实际,但对于双正则稀疏问题,它们是有效的。双不规则稀疏线性系统有着广泛的应用,佛罗里达大学收集的非对称矩阵中有24.4%是双不规则稀疏矩阵。对于这类问题,我们提出了一种变换方法,该方法由四个步骤组成:(i)将给定的双不规则稀疏问题变换为具有相同系数矩阵a - 1的少量双正则稀疏问题;(ii)使用SPAI, PSAI (t - 1)和RSAI (t - 1)构造a - 1的稀疏近似逆M; (iii)用Krylov解算器求解预设的双正则稀疏线性系统。(iv)从双正则稀疏问题的近似解中恢复出具有规定精度的原始问题的近似解。转换方法考虑了许多理论和实践问题。在许多实际问题上的数值实验证实了转换方法相对于标准方法的非常明显的优越性,标准方法是用SPAI、PSAI (t - 1)或RSAI (t - 1)对原始的双不规则稀疏问题进行先决条件,并用Krylov解算器对得到的先决系统进行求解。
A sparse matrix is called double irregular sparse if it has at least one relatively dense column and row, and it is double regular sparse if all the columns and rows of it are sparse. The sparse approximate inverse preconditioning procedures SPAI, PSAI (t o l) and RSAI (t o l) are costly and even impractical to construct preconditioners for a large sparse nonsymmetric linear system with the coefficient matrix being double irregular sparse, but they are efficient for double regular sparse problems. Double irregular sparse linear systems have a wide range of applications, and 24.4% of the nonsymmetric matrices in the Florida University collection are double irregular sparse. For this class of problems, we propose a transformation approach, which consists of four steps:(i) transform a given double irregular sparse problem into a small number of double regular sparse ones with the same coefficient matrix A ˆ,(ii) use SPAI, PSAI (t o l) and RSAI (t o l) to construct sparse approximate inverses M of A ˆ,(iii) solve the preconditioned double regular sparse linear systems by Krylov solvers, and (iv) recover an approximate solution of the original problem with a prescribed accuracy from those of the double regular sparse ones. A number of theoretical and practical issues are considered on the transformation approach. Numerical experiments on a number of real-world problems confirm the very sharp superiority of the transformation approach to the standard approach that preconditions the original double irregular sparse problem by SPAI, PSAI (t o l) or RSAI (t o l) and solves the resulting preconditioned system by Krylov solvers.
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