广义给定旋转在复联合特征值分解中的应用

A. Mesloub
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引用次数: 1

摘要

本文给出了在复联合特征值分解(JEVD)问题中使用广义给定旋转的不同方法。给出了广义给定旋转的不同格式,证明了所引入的近似,并重点介绍了将针对实JEVD开发的算法扩展到复JEVD的过程。一些联合对角化问题采用广义Givens旋转来求解,文献中有许多在实际情况下开发的算法,并没有推广到复杂情况。因此,我们在这里给出了一种简单而不平凡的方法来从实际情况中得到复杂的情况。仿真结果强调了所提出的技术在不同场景下的有效性和行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generalized Givens Rotations Applied to Complex Joint Eigenvalue Decomposition
This paper shows the different ways of using generalized Givens rotations in complex joint eigenvaluedecomposition (JEVD) problem. It presents the different schemes of generalized Givens rotation, justifies the introducedapproximations and focuses on the process of extending an algorithm developed for real JEVD to the complex JEVD.Several Joint Diagonalization problem use generalized Givens rotations to achieve the solution, many algorithmsdeveloped in the real case exist in the literature and are not generalized to the complex case. Hence, we show herein asimple and not trivial way to get the complex case from the real one. Simulation results are provided to highlight theeffectiveness and behaviour of the proposed techniques for different scenarios.
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