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首页> 外文期刊>SIAM Journal on Matrix Analysis and Applications >Eigendecomposition of the discrete double-curl operator with application to fast eigensolver for three-dimensional photonic crystals
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Eigendecomposition of the discrete double-curl operator with application to fast eigensolver for three-dimensional photonic crystals

机译:离散双曲线算子的特征分解及其在三维光子晶体快速本征求解器中的应用

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摘要

This article focuses on the discrete double-curl operator arising in the Maxwell equation that models three-dimensional photonic crystals with face-centered cubic lattice. The discrete double-curl operator is the degenerate coefficient matrix of the generalized eigenvalue problems (GEVP) due to the Maxwell equation. We derive an eigendecomposition of the degenerate coefficient matrix and explore an explicit form of orthogonal basis for the range and null spaces of this matrix. To solve the GEVP, we apply these theoretical results to project the GEVP to a standard eigenvalue problem (SEVP), which involves only the eigenspace associated with the nonzero eigenvalues of the GEVP, and therefore the zero eigenvalues are excluded and will not degrade the computational efficiency. This projected SEVP can be solved efficiently by the inverse Lanczos method. The linear systems within the inverse Lanczos method are well-conditioned and can be solved efficiently by the conjugate gradient method without using a preconditioner. We also demonstrate how two forms of matrix-vector multiplications, which are the most costly part of the inverse Lanczos method, can be computed by fast Fourier transformation due to the eigendecomposition to significantly reduce the computation cost. Integrating all of these findings and techniques, we obtain a fast eigenvalue solver. The solver has been implemented by MATLAB and successfully solves each of a set of 5.184 million dimension eigenvalue problems within 50 to 104 minutes on a workstation with two Intel Quad-Core Xeon X5687 3.6 GHz CPUs.
机译:本文关注于麦克斯韦方程式中产生的离散双卷曲算子,该算子对具有面心立方晶格的三维光子晶体进行了建模。离散双曲线算子是由于麦克斯韦方程引起的广义特征值问题(GEVP)的退化系数矩阵。我们推导退化系数矩阵的特征分解,并探索该矩阵的范围和零空间的正交形式的显式形式。为了解决GEVP,我们将这些理论结果应用到标准特征值问题(SEVP)中,该问题仅涉及与GEVP的非零特征值相关的特征空间,因此零特征值被排除在外,不会降低计算量。效率。该预测的SEVP可以通过反Lanczos方法有效求解。 Lanczos逆方法中的线性系统条件良好,可以通过共轭梯度法有效解决,而无需使用前置条件。我们还演示了如何通过特征分解来通过快速傅立叶变换来计算矩阵向量乘法的两种形式,这是逆Lanczos方法最昂贵的部分,从而显着降低了计算成本。综合所有这些发现和技术,我们获得了一个快速的特征值求解器。该求解器已由MATLAB实现,并在具有两个Intel Quad-Core Xeon X5687 3.6 GHz CPU的工作站上,在50至104分钟内成功解决了518.4万维特征值问题。

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