In this work, we propose a simple yet generic preconditioned Krylov subspace method for a large class of nonsymmetric block Toeplitz all-at-once systems arising from discretizing evolutionary partial differential equations. Namely, our main result is a novel symmetric positive definite preconditioner, which can be efficiently diagonalized by the discrete sine transform matrix. More specifically, our approach is to first permute the original linear system to obtain a symmetric one, and subsequently develop a desired preconditioner based on the spectral symbol of the modified matrix. Then, we show that the eigenvalues of the preconditioned matrix sequences are clustered around $\pm 1$, which entails rapid convergence, when the minimal residual method is devised. Alternatively, when the conjugate gradient method on normal equations is used, we show that our preconditioner is effective in the sense that the eigenvalues of the preconditioned matrix sequence are clustered around the unity. An extension of our proposed preconditioned method is given for high-order backward difference time discretization schemes, which applies on a wide range of time-dependent equations. Numerical examples are given to demonstrate the effectiveness of our proposed preconditioner, which consistently outperforms an existing block circulant preconditioner discussed in the relevant literature.
翻译:在这项工作中,我们提出一个简单而通用的、具有先决条件的Krylov 子空间方法,用于由分解进化部分差异方程式产生的大量非对称区块Teeplitz全自动自动调整系统。也就是说,我们的主要结果是一个新的对称正正确定先决条件,可以通过离散正弦变形矩阵进行高效的分解。更具体地说,我们的做法是首先对原始线性系统进行对称,然后根据修改后的矩阵的光谱符号开发一个理想的前提条件。然后,我们表明,在设计最低残余法时,先决条件矩阵序列的精度值将围绕$\pm 1美元,这需要快速趋同。或者,当使用离散正正正正正方程式的同梯度法时,我们证明我们的先决条件是有效的,即:先决条件矩阵序列的灵精度值将围绕统一组合在一起。我们提出的先决条件方法的延伸是高阶后向偏差时间分解方案,在所给出的高度偏差时间分立的模型中,这需要快速趋近的趋近的模型将适用于我们所讨论到一个长期具有时间性的先决条件性的基本模型。