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arXiv 1812.04701math.NAcs.NAphysics.comp-ph
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快速非线性傅里叶变换的数值方法,第一部分:指数Runge-Kutta方法和线性多步方法

Numerical Methods for Fast Nonlinear Fourier Transformation, Part I: Exponential Runge-Kutta and Linear Multistep Methods

Delft University of Technology(代尔夫特理工大学)

Vishal Vaibhav

AI总结:

本文研究了用于快速非线性傅里叶变换(NFT)的数值方法,重点分析了指数Runge-Kutta(RK)和线性多步(LM)方法的稳定性与收敛性,并展示了这些方法在计算散射系数时的复杂度为O(N log²N)以及收敛率O(N⁻ᵖ)。

摘要:

本文系列论文的主要目标是探索指数积分器类中用于快速非线性傅里叶变换(NFT)的数值方法的整个领域。在本文中,我们探讨了指数Runge-Kutta(RK)和线性多步(LM)方法的理论方面,特别是通过转移矩阵公式分析这些方法的稳定性与收敛性。本文的分析表明,尽管指数LM方法天然适合基于FFT的快速多项式运算,但RK方法需要等距节点才能实现这一点。因此,每种方法家族都能产生一组快速NFT算法,使得散射系数的计算复杂度为O(N log²N),收敛率为O(N⁻ᵖ),其中N是信号样本数,p是底层离散化方案的阶数。此外,尽管RK方法可以处理消失型和周期型边界条件,但LM方法只能处理前者,而无需起始过程。本文提出的想法自然扩展到被称为通用线性方法的积分器家族,将在后续论文中探讨。

英文摘要:

The main objective of this series of papers is to explore the entire landscape of numerical methods for fast nonlinear Fourier transformation (NFT) within the class of integrators known as the exponential integrators. In this paper, we explore the theoretical aspects of exponential Runge-Kutta (RK) and linear multistep (LM) methods, in particular, the stability and convergence of these methods via the transfer matrix formulation. The analysis carried out in the paper shows that while the exponential LM methods are naturally amenable to FFT-based fast polynomial arithmetic, the RK methods require equispaced nodes to achieve that. Therefore, each these family of methods is capable of yielding a family of fast NFT algorithms such that the scattering coefficients can be computed with a complexity of $\mathscr{O}(N\log^2N)$ and a rate of convergence given by $\mathscr{O}(N^{-p})$ where $N$ is the number of samples of the signal and $p$ is order of the underlying discretization scheme. Further, while RK methods can accommodate vanishing as well as periodic boundary conditions, the LM methods can only handle the former type of boundary conditions without requiring a starting procedure. The ideas presented in this paper extend naturally to the family of integrators known as general linear methods which will be explored in a forthcoming paper.

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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