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A method based on Rayleigh quotient gradient flow for extreme and interior eigenvalue problems

机译:基于瑞利商梯度流的极值和内在特征值问题的方法

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摘要

Recently, a continuous method has been proposed by Golub and Liao as an alternative way to solve the minimum and interior eigenvalue problems. According to their numerical results, their method seems promising. This article is an extension along this line. In this article, firstly, we convert an eigenvalue problem to an equivalent constrained optimization problem. Secondly, using the Karush-Kuhn-Tucker conditions of this equivalent optimization problem, we obtain a variant of the Rayleigh quotient gradient flow, which is formulated by a system of differential-algebraic equations. Thirdly, based on the Rayleigh quotient gradient flow, we give a practical numerical method for the minimum and interior eigenvalue problems. Finally, we also give some numerical experiments of our method, the Golub and Liao method, and EIGS (a Matlab implementation for computing eigenvalues using restarted Arnoldi's method) for some typical eigenvalue problems. Our numerical experiments indicate that our method seems promising for most test problems.
机译:最近,Golub和Liao提出了一种连续方法,作为解决最小和内部特征值问题的替代方法。根据他们的数值结果,他们的方法似乎很有希望。本文是这方面的延伸。在本文中,首先,我们将特征值问题转换为等效的约束优化问题。其次,使用这个等效优化问题的Karush-Kuhn-Tucker条件,我们获得了瑞利商梯度流的变体,它是由微分代数方程组公式化的。第三,基于瑞利商梯度流,给出了最小特征值和内部特征值问题的实用数值方法。最后,我们还给出了一些典型特征值问题的方法,Golub和Liao方法以及EIGS(使用重新启动的Arnoldi方法来计算特征值的Matlab实现)的数值实验。我们的数值实验表明,对于大多数测试问题,我们的方法似乎很有希望。

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