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A Class of Efficient Preconditioners with Multilevel Sequentially Semiseparable Matrix Structure

机译:一类具有多级的高效预处理器,依次可分析矩阵结构

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This paper presents a class of preconditioners for sparse systems arising from discretized partial differential equations (PDEs). In this class of preconditioners, we exploit the multilevel sequentially semiseparable (MSSS) structure of the system matrix. The off-diagonal blocks of MSSS matrices are of low-rank, which enables fast computations of linear complexity. In order to keep the low-rank property of the off-diagonal blocks, model reduction algorithm is necessary. We tested our preconditioners for 2D convection-diffusion equation, the computational results show the excellent performance of this approach.
机译:本文介绍了一类用于由离散的部分微分方程(PDE)产生的稀疏系统的预处理器。在这类的预处理器中,我们利用了系统矩阵的多级半可拆动(MSSS)结构。 MSS矩阵的非对角线块具有低秩,其能够快速计算线性复杂性。为了保持非对角线块的低秩属性,需要模型减少算法。我们测试了我们的预处理器进行2D对流 - 扩散方程,计算结果显示了这种方法的优异性能。

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