arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.07555math.NAcs.NA
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多级谱延迟改正的收敛性分析

Convergence analysis of multi-level spectral deferred corrections

Gitte Kremling, Robert Speck

摘要:

谱延迟校正(SDC)方法是求解常微分方程(ODE)的一类迭代算法。它可以解释为配置问题的预条件Picard迭代。这种方法的收敛性是众所周知的,对于适当的问题,它获得一个阶,每次迭代的阶的配置问题的求积方法提供。这个吸引人的功能,使一个容易创建灵活的,高阶精度的方法常微分方程。SDC的一种变体是多级光谱延迟校正(MLSDC)。在这里,迭代是在一个层次上进行的,FAS校正项,如在非线性多重网格方法中,耦合不同层次上的解决方案。虽然有几个数值例子表明它的能力和效率,理论上的收敛性证明仍然缺乏。本文探讨了这一问题。文中给出了MLSDC收敛性的证明,包括确定时间-步长的收敛速度,并给出了数值结果。结果表明,在收敛速度方面,该方法相对于SDC的优势受到限制。

英文摘要:

The spectral deferred correction (SDC) method is class of iterative solvers for ordinary differential equations (ODEs). It can be interpreted as a preconditioned Picard iteration for the collocation problem. The convergence of this method is well-known, for suitable problems it gains one order per iteration up to the order of the quadrature method of the collocation problem provided. This appealing feature enables an easy creation of flexible, high-order accurate methods for ODEs. A variation of SDC are multi-level spectral deferred corrections (MLSDC). Here, iterations are performed on a hierarchy of levels and an FAS correction term, as in nonlinear multigrid methods, couples solutions on different levels. While there are several numerical examples which show its capabilities and efficiency, a theoretical convergence proof is still missing. This paper addresses this issue. A proof of the convergence of MLSDC, including the determination of the convergence rate in the time-step size, will be given and the results of the theoretical analysis will be numerically demonstrated. It turns out that there are restrictions for the advantages of this method over SDC regarding the convergence rate.

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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