In this work, an approximate family of implicit multiderivative Runge-Kutta (MDRK) time integrators for stiff initial value problems is presented. The approximation procedure is based on the recent Approximate Implicit Taylor method (Baeza et al. in Comput. Appl. Math. 39:304, 2020). As a Taylor method can be written in MDRK format, the novel family constitutes a multistage generalization. Two different alternatives are investigated for the computation of the higher order derivatives: either directly as part of the stage equation, or either as a separate formula for each derivative added on top of the stage equation itself. From linearizing through Newton's method, it turns out that the conditioning of the Newton matrix behaves significantly different for both cases. We show that direct computation results in a matrix with a conditioning that is highly dependent on the stiffness, increasing exponentially in the stiffness parameter with the amount of derivatives. Adding separate formulas has a more favorable behavior, the matrix conditioning being linearly dependent on the stiffness, regardless of the amount of derivatives. Despite increasing the Newton system significantly in size, through several numerical results it is demonstrated that doing so can be considerably beneficial.
翻译:在这项工作中,介绍了一个隐含的多发性龙格-库塔(Mdrack)时间集成器的近似组合,用于解决初始价值问题。近似程序基于最近的Appbear Inplicit Taylor方法(Baeza等人在Comput. Appl. Math. 39:304,2020)。泰勒方法可以以McRK格式写成,新奇家族是一个多阶段通用的方法。在计算较高顺序衍生物时,对两种不同的选择进行了调查:直接作为阶段方程的一部分,或作为阶段方程本身上添加的每种衍生物的单独公式。从线性化到牛顿方法,结果显示牛顿矩阵的调节条件在两种情况下都大不相同。我们显示,直接计算结果在矩阵中,一个高度依赖坚硬度,在坚硬度参数上与衍生物数量成指数增长。添加不同的公式具有更有利的行为,而矩阵的线性调节则取决于级方程度,而不管衍生物的数量如何。尽管牛顿系统在规模上大幅增长,但通过几个数字结果证明它非常有益。