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Global error estimation with adaptive explicit Runge-Kutta methods

机译:自适应显式Runge-Kutta方法进行全局误差估计

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摘要

Users of locally-adaptive software for initial value ordinary differential equations are likely to be concerned with global errors. At the cost of extra computation, global error estimation is possible. Zadunaisky's method and `solving for the error estimate' are two techniques that have been successfully incorporated into Runge-Kutta algorithms. The standard error analysis for these techniques, however, does not take account of the stepsize selection mechanism. In this paper, some new results are presented which, under suitable assumptions show that these techniques are asymptotically valid when used with an adaptive, variable stepsize algorithm - the global error estimate reproduces the leading term of the global error in the limit as the error tolerance tends to zero. The analysis is also applied to Richardson extrapolation (step halving). Numerical results are provided for the technique of solving for the error estimate with several Runge - Kutta mmethods of Dormand, Lockyer, McGorrigan and Prince.
机译:初始值常微分方程的本地自适应软件的用户可能会担心全局误差。以额外的计算为代价,可以进行全局误差估计。 Zadunaisky的方法和“求解误差估计”是已成功整合到Runge-Kutta算法中的两种技术。但是,这些技术的标准误差分析未考虑步长选择机制。本文提出了一些新的结果,这些结果在适当的假设下表明,当这些技术与自适应可变步长算法一起使用时,它们是渐近有效的-全局误差估计在极限内重现了全局误差的前项,作为误差容限趋于零。该分析还应用于理查森外推(步骤减半)。数值结果提供了使用Dormand,Lockyer,McGorrigan和Prince的几种Runge-Kutta方法求解误差估计的技术。

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