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

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

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.

Original languageEnglish
Article number47
Number of pages24
JournalJournal of Scientific Computing
Volume103
Issue number2
Early online date29 Mar 2025
DOIs
Publication statusE-pub ahead of print - 29 Mar 2025

User-Defined Keywords

  • All-at-once systems
  • Crank–Nicolson
  • Multilevel Toeplitz matrices
  • Parallel-in-time
  • Preconditioning
  • θ-Method

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