Stability implies robust convergence of a class of preconditioned parallel-in-time iterative algorithms
Shu-Lin Wu, Tao Zhou, Zhi Zhou

TL;DR
This paper provides a unified convergence analysis for a class of parallel-in-time iterative algorithms based on block α-circulant preconditioning, showing that stability of the time integrator guarantees robust convergence regardless of step size or spectrum.
Contribution
It introduces a general convergence bound for preconditioned parallel-in-time algorithms applicable to various time integrators, independent of specific spectral properties.
Findings
Convergence rate is bounded by α/(1-α) for one-step methods.
Bound becomes cα/(1-cα) for linear multistep methods, with c ≥ 1.
Proof relies only on the stability of the time integrator, independent of step size and spectrum.
Abstract
Solving evolutionary equations in a parallel-in-time manner is an attractive topic and many algorithms are proposed in recent two decades. The algorithm based on the block -circulant preconditioning technique has shown promising advantages, especially for wave propagation problems. By fast Fourier transform for factorizing the involved circulant matrices, the preconditioned iteration can be computed efficiently via the so-called diagonalization technique, which yields a direct parallel implementation across all time levels. In recent years, considerable efforts have been devoted to exploring the convergence of the preconditioned iteration by studying the spectral radius of the iteration matrix, and this leads to many case-by-case studies depending on the used time-integrator. In this paper, we propose a unified convergence analysis for the algorithm applied to , where…
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Taxonomy
TopicsMatrix Theory and Algorithms · Numerical methods for differential equations · Advanced Numerical Methods in Computational Mathematics
