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arXiv 0907.4631math.NAcs.NA
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用于评估指数积分器中出现的phi函数的Krylov子空间算法

A Krylov subspace algorithm for evaluating the phi-functions appearing in exponential integrators

University of Leeds(利兹大学);La Trobe University(乐卓博大学)

Jitse Niesen, Will M. Wright

AI总结:

提出一种基于Krylov子空间的自适应算法,通过Arnoldi或Lanczos迭代计算矩阵指数及其相关phi函数作用在向量上的结果,用于高效求解线性常微分方程组,并在Matlab函数phipm中实现,数值实验表明其显著优于现有方法。

摘要:

我们开发了一种算法,用于计算具有多项式非齐次项的大型线性常微分方程组(ODEs)的解。这等价于计算某个矩阵函数作用于表示初始条件的向量上的结果。该矩阵函数是矩阵指数和与指数相关的其他函数(即所谓的phi函数)的线性组合。此类计算是指数积分器实现中的主要计算负担,而指数积分器可以求解一般的常微分方程。我们的方法是通过使用Arnoldi或Lanczos迭代构造Krylov子空间,并将函数投影到该子空间上来计算矩阵函数的作用。这结合了时间步进以防止Krylov子空间变得过大。该算法是完全自适应的:它同时改变时间步长和Krylov子空间的维度以达到所需的精度。我们在Matlab函数phipm中实现了该算法,并给出了如何获取和使用该函数的说明。各种数值实验表明,phipm函数通常比现有技术显著更高效。

英文摘要:

We develop an algorithm for computing the solution of a large system of linear ordinary differential equations (ODEs) with polynomial inhomogeneity. This is equivalent to computing the action of a certain matrix function on the vector representing the initial condition. The matrix function is a linear combination of the matrix exponential and other functions related to the exponential (the so-called phi-functions). Such computations are the major computational burden in the implementation of exponential integrators, which can solve general ODEs. Our approach is to compute the action of the matrix function by constructing a Krylov subspace using Arnoldi or Lanczos iteration and projecting the function on this subspace. This is combined with time-stepping to prevent the Krylov subspace from growing too large. The algorithm is fully adaptive: it varies both the size of the time steps and the dimension of the Krylov subspace to reach the required accuracy. We implement this algorithm in the Matlab function phipm and we give instructions on how to obtain and use this function. Various numerical experiments show that the phipm function is often significantly more efficient than the state-of-the-art.

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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