Motivated by a wide range of real-world problems whose solutions exhibit boundary and interior layers, the numerical analysis of discretizations of singularly perturbed differential equations is an established sub-discipline within the study of the numerical approximation of solutions to differential equations. Consequently, much is known about how to accurately and stably discretize such equations on a priori adapted meshes, in order to properly resolve the layer structure present in their continuum solutions. However, despite being a key step in the numerical simulation process, much less is known about the efficient and accurate solution of the linear systems of equations corresponding to these discretizations. In this paper, we develop a preconditioning strategy that is tuned to the matrix structure induced by using layer-adapted meshes for convection-diffusion equations, proving a strong condition-number bound on the preconditioned system in one spatial dimension, and a weaker bound in two spatial dimensions. Numerical results confirm the efficiency of the resulting preconditioners in one and two dimensions, with time-to-solution of less than one second for representative problems on $1024\times 1024$ meshes and up to $40\times$ speedup over standard sparse direct solvers.
翻译:在一系列现实世界问题的推动下,解决方案呈现出边界和内部层,对奇不测差异方程的离散性进行数字分析是研究差异方程解决方案的数值近似值的既定次级纪律,因此,对于如何准确和稳定地将此类方程分解在先验调整的模件上,以适当解决其连续解决方案中存在的层结构,人们知道的很多,尽管这是数字模拟进程中的一个关键步骤,但对于与这些离散性相对应的方程的线性系统的效率和准确性解决办法却知之甚少。在本文件中,我们制定了一种符合矩阵结构的前提条件战略,即使用层调整的模子模件来进行对等-增异方程方程,证明一个空间层面的前提条件系统有很强的条件数量,两个空间层面的束缚较弱。数字结果证实,由此产生的先决条件在一个和两个层面的效率,在1024美元/美元/1024美元/美元/1024美元/平流速度上代表问题的时间到不到第二位数。