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arXiv 1607.01733math.NAcs.NA
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Toeplitz矩阵矩阵指数的快速计算

Fast computation of the matrix exponential for a Toeplitz matrix

École Polytechnique Fédérale de Lausanne(洛桑联邦理工学院)

Daniel Kressner, Robert Luce

AI总结:

本文提出了一种快速计算Toeplitz矩阵指数的算法,利用缩放和平方框架,并结合有理逼近理论和低位移秩矩阵的分析,实现了在特定谱条件下具有二次复杂度的高效方法。

摘要:

矩阵指数的计算是数值数学中普遍存在的操作,对于一般的n×n矩阵,其计算复杂度为O(n³)。当输入矩阵为Toeplitz矩阵时,例如作为具有时间不变核的积分方程离散化结果时,如何利用Toeplitz结构成为一个有趣的问题,因为Toeplitz矩阵的指数通常不是Toeplitz矩阵。本文的主要贡献是提出了快速计算Toeplitz矩阵指数的算法。这些算法在谱为实数、扇形或更一般情况下,即右端特征值的虚部变化不大时,具有可证明的二次复杂度。即使在这些谱约束之外,它们也可能高效。这些算法基于缩放和平方框架,其分析将经典有理逼近理论的结果与低位移秩矩阵联系起来。作为示例,所开发的方法应用于Merton的跳跃扩散模型以期权定价。

英文摘要:

The computation of the matrix exponential is a ubiquitous operation in numerical mathematics, and for a general, unstructured $n\times n$ matrix it can be computed in $\mathcal{O}(n^3)$ operations. An interesting problem arises if the input matrix is a Toeplitz matrix, for example as the result of discretizing integral equations with a time invariant kernel. In this case it is not obvious how to take advantage of the Toeplitz structure, as the exponential of a Toeplitz matrix is, in general, not a Toeplitz matrix itself. The main contribution of this work are fast algorithms for the computation of the Toeplitz matrix exponential. The algorithms have provable quadratic complexity if the spectrum is real, or sectorial, or more generally, if the imaginary parts of the rightmost eigenvalues do not vary too much. They may be efficient even outside these spectral constraints. They are based on the scaling and squaring framework, and their analysis connects classical results from rational approximation theory to matrices of low displacement rank. As an example, the developed methods are applied to Merton's jump-diffusion model for option pricing.

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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