In this article, we derive fast and robust preconditioned iterative methods for the all-at-once linear systems arising upon discretization of time-dependent PDEs. The discretization we employ is based on a Runge--Kutta method in time, for which the development of robust solvers is an emerging research area in the literature of numerical methods for time-dependent PDEs. By making use of classical theory of block matrices, one is able to derive a preconditioner for the systems considered. An approximate inverse of the preconditioner so derived consists in a fixed number of linear solves for the system of the stages of the method. We thus propose a preconditioner for the latter system based on a singular value decomposition (SVD) of the (real) Runge--Kutta matrix $A_{\mathrm{RK}} = U \Sigma V^\top$. Supposing $A_{\mathrm{RK}}$ is invertible, we prove that the spectrum of the system for the stages preconditioned by our SVD-based preconditioner is contained within the right-half of the unit circle, under suitable assumptions on the matrix $U^\top V$ (which is well defined due to the polar decomposition of $A_{\mathrm{RK}}$). We show the numerical efficiency of our SVD-based preconditioner by solving the system of the stages arising from the discretization of the heat equation and the Stokes equations, with sequential time-stepping. Finally, we provide numerical results of the all-at-once approach for both problems.
翻译:在本篇文章中,我们为基于时间的 PDE 离散产生的全天线性系统获取了快速和稳健的、具有先决条件的迭代方法。我们使用的离散方法基于一个及时的Runge-Kutta 方法,为此,开发强大的解析器是基于时间的 PDE 数字方法文献中的一个新兴研究领域。通过使用典型的区块矩阵理论,人们能够为所考虑的系统获得一个先决条件。与此相反,所得出的前提条件是该方法各个阶段系统的一个固定数量的线性解析办法。因此,我们提出后一个系统的先决条件是基于(真实的) Runge-Kutta 矩阵的单值解析(SVVD ) 方法,为此, Rungege-Kutta 矩阵的单值解析( $amathrm{RK} = U\Sgmagma V<unk> t$。 Supposing $的Stromacle 和我们基于SV-D 美元正值的平方平面的平方块的平面图, 最终的假设中,我们Slo- dal- dal-rupal-rational-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx</s>