We analyze and optimize two-level methods applied to a symmetric interior penalty discontinuous Galerkin finite element discretization of a singularly perturbed reaction-diffusion equation. Previous analyses of such methods have been performed numerically by Hemker et. al. for the Poisson problem. Our main innovation is that we obtain explicit formulas for the optimal relaxation parameter of the two-level method for the Poisson problem in 1D, and very accurate closed form approximation formulas for the optimal choice in the reaction-diffusion case in all regimes. Our Local Fourier Analysis, which we perform at the matrix level to make it more accessible to the linear algebra community, shows that for DG penalization parameter values used in practice, it is better to use cell block-Jacobi smoothers of Schwarz type, in contrast to earlier results suggesting that point block-Jacobi smoothers are preferable, based on a smoothing analysis alone. Our analysis also reveals how the performance of the iterative solver depends on the DG penalization parameter, and what value should be chosen to get the fastest iterative solver, providing a new, direct link between DG discretization and iterative solver performance. We illustrate our analysis with numerical experiments and comparisons in higher dimensions and different geometries.
翻译:我们分析并优化了用于对称内部刑罚不连续加勒金(Galerkin)不连续反应扩散方程式的两层方法。 以前,Hemker等人对Poisson 问题进行了数字分析。 我们的主要创新是,我们获得用于Poisson 1D 问题双层方法最佳放松参数的明确公式,以及用于所有系统反应扩散案例最佳选择的非常精确的封闭式近似公式。我们用矩阵水平进行的本地四倍分析,使线性代数群别更容易获得。我们进行的本地四倍分析显示,对于DG在实践中使用的惩罚性参数值,最好使用Schwarz型的Jacobi细胞块平滑动器,而早先的结果则表明,光靠平滑的分析,点式的Jacobi平滑动器更可取。我们的分析还表明,迭代解器的性能如何取决于DG惩罚性参数,以及应该选择何种价值来获得最快的迭代数解解解器,提供新的、直接的磁度分析,并用我们不同程度的分辨率分析。