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.
翻译:光谱推迟校正(SDC)方法是普通差异方程式(ODEs)的迭代求解器类别。它可以被解释为合用问题的一个先决条件的Picard迭代。这种方法的趋同是众所周知的,因为每个迭代方法在提供合用问题之类方法的顺序上都有一个顺序,而每迭代方法在所提供的合用问题之类方法的顺序上却有一个顺序。这一吸引性特征使得很容易为ODE创造灵活、高顺序的准确方法。SDC的变异是多层次的光谱推迟校正(MLSDC)。这里,迭代是按等级等级的等级和FAS校正术语进行,如非线性多格方法,在不同级别上采用夫妇解决办法。虽然有若干数字例子表明其能力和效率,但理论上的趋同证据仍然缺失。该文件将解决这个问题,将证明MLSDC的趋同程度,包括确定在时间段内的趋同率,理论分析的结果将以数字显示。它表明,这一方法在比SDC的趋同率方面优于SDC的优势是有限制。