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arXiv 2408.03535math.NAcs.NA
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时间离散采用θ-方法的一类演化偏微分方程的高效预条件子

An efficient preconditioner for evolutionary partial differential equations with $θ$-method in time discretization

Yuan-Yuan Huang, Po Yin Fung, Sean Y. Hon, Xue-Lei Lin

AI总结:

本文针对θ-方法时间离散的演化偏微分方程,提出一种可时间并行实现的高效单侧预条件子,并通过双侧预条件系统的条件数一致有界性证明保证GMRES收敛,数值实验验证了其有效性与鲁棒性。

摘要:

本研究采用θ-方法对一类演化偏微分方程进行离散。随后,我们对所得的全同时(all-at-once)线性系统进行变换,并引入一种新型单侧预条件子,该预条件子能够以时间并行方式快速实现。通过引入一个辅助的双侧预条件系统,我们从理论上揭示了广义极小残量(GMRES)方法分别应用于单侧和双侧预条件系统时残差之间的关系。此外,我们证明双侧预条件矩阵的条件数由一个与矩阵规模无关的常数一致上界所界定,这进而保证了GMRES方法在单侧预条件系统上的收敛行为。数值实验验证了所提预条件方法的效率和鲁棒性。

英文摘要:

In this study, the $θ$-method is used for discretizing a class of evolutionary partial differential equations. Then, we transform the resultant all-at-once linear system and introduce a novel one-sided preconditioner, which can be fast implemented in a parallel-in-time way. By introducing an auxiliary two-sided preconditioned system, we provide theoretical insights into the relationship between the residuals of the generalized minimal residual (GMRES) method when applied to both one-sided and two-sided preconditioned systems. Moreover, we show that the condition number of the two-sided preconditioned matrix is uniformly bounded by a constant that is independent of the matrix size, which in turn implies that the convergence behavior of the GMRES method for the one-sided preconditioned system is guaranteed. Numerical experiments confirm the efficiency and robustness of the proposed preconditioning approach.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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