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A Subgraph Preconditioning Algorithm for Large Linear Systems

机译:大型线性系统的子图预处理算法

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摘要

In this paper, a preconditioning algorithm for sparse, symmetric, diagonally dominant (SDD) linear systems is proposed by using combinatorial techniques. Firstly, we construct preconditioners by finding a subgraph based on the relationship between SDD matrices and undirected graphs. The subgraph is built by augmenting a low-stretch spanning tree with some extra high stretch edges. Then we implement the algorithm for building a low stretch tree and give a parallel implementation of the subgraph preconditioning algorithm based on PETSc software. Finally, numerical experiments arising from both elliptic PDEs and Laplacian systems of network graphs are tested to evaluate the performance of our algorithm. Numerical experiments show that preconditioners constructed by our algorithm are more efficient than incomplete Choleskey factorization preconditioners and Vaidya's preconditioners. Besides, based on the tree structure, our preconditioners perform respectable parallel scalability.
机译:本文采用组合技术提出了一种用于稀疏,对称的,对角占主导地位(SDD)线性系统的预处理算法。 首先,通过基于SDD矩阵与未向图形的关系来查找子图来构建预处理器。 该子图是通过增强一个带有一些超高拉伸边缘的低伸展生成树而构建的。 然后,我们实现了构建低拉伸树的算法,并基于PETSC软件对子图预处理算法的并行实现。 最后,测试了来自椭圆PDE和网络图的拉普拉斯系统的数值实验,以评估我们算法的性能。 数值实验表明,由我们的算法构建的前提者比不完整的Choleskey分解预处理者和Vaidya的预处理者更有效。 此外,基于树结构,我们的预处理器执行可观的并行可扩展性。

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