An Adaptive, Highly Accurate and Efficient, Parker-Sochacki Algorithm for Numerical Solutions to Initial Value Ordinary Differential Equation Systems

Jenna Guenther
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引用次数: 2

Abstract

6 The Parker-Sochacki Method (PSM) allows the numerical approximation of solutions to a polynomial 7 initial value ordinary differential equation or system (IVODE) using an algebraic power series method. PSM 8 is equivalent to a modified Picard iteration and provides an efficient, recursive computation of the coefficients 9 of the Taylor polynomial at each step. To date, PSM has largely concentrated on fixed step methods. We 10 develop and test an adaptive stepping scheme that, for many IVODEs, enhances the accuracy and efficiency of 11 PSM. PSM Adaptive (PSMA) is compared to its fixed step counterpart and to standard Runge-Kutta (RK) 12 foundation algorithms using three example IVODEs. In comparison, PSMA is shown to be competitive, often 13 outperforming these methods in terms of accuracy, number of steps, and execution time. A library of functions 14 is also presented that allows access to PSM techniques for many non-polynomial IVODEs without having to 15 first rewrite these in the necessary polynomial form, making PSM a more practical tool. 16
初值常微分方程数值解的自适应、高精度、高效的Parker-Sochacki算法
Parker-Sochacki方法(PSM)允许使用代数幂级数方法对多项式初值常微分方程或系统(IVODE)的解进行数值逼近。PSM 8相当于改进的Picard迭代,并在每一步提供对泰勒多项式系数9的有效递归计算。迄今为止,PSM主要集中在固定步骤方法上。我们开发并测试了一种自适应步进方案,对于许多IVODEs,提高了11 PSM的精度和效率。使用三个示例IVODEs将PSM自适应(PSMA)与固定步长对应算法和标准龙格-库塔(RK) 12基础算法进行比较。相比之下,PSMA显示出竞争力,通常在准确性、步骤数和执行时间方面优于这些方法。还提供了一个函数库14,允许访问许多非多项式IVODEs的PSM技术,而不必首先将它们重写为必要的多项式形式,使PSM成为一个更实用的工具。16
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